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.

test_fmvsm_n_1920		97.55 11	97.89 3	96.53 79	98.41 74	91.73 107	98.01 57	99.02 1	96.37 4	99.30 1	98.92 10	92.39 37	99.79 33	99.16 5	99.46 39	98.08 166

PGM-MVS		96.81 42	96.53 50	97.65 41	99.35 20	93.53 58	97.65 106	98.98 2	92.22 131	97.14 52	98.44 44	91.17 62	99.85 18	94.35 113	99.46 39	99.57 26

MVS_111021_HR		96.68 51	96.58 49	96.99 68	98.46 70	92.31 91	96.20 244	98.90 3	94.30 60	95.86 102	97.74 104	92.33 38	99.38 113	96.04 63	99.42 46	99.28 65

test_fmvsmconf_n		97.49 12	97.56 9	97.29 53	97.44 140	92.37 88	97.91 75	98.88 4	95.83 8	98.92 12	99.05 5	91.45 53	99.80 30	99.12 6	99.46 39	99.69 12

ACMMP		96.27 63	95.93 65	97.28 55	99.24 28	92.62 80	98.25 36	98.81 5	92.99 107	94.56 131	98.39 48	88.96 89	99.85 18	94.57 112	97.63 132	99.36 60
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
MVS_111021_LR		96.24 64	96.19 63	96.39 95	98.23 91	91.35 127	96.24 242	98.79 6	93.99 67	95.80 104	97.65 111	89.92 80	99.24 124	95.87 67	99.20 71	98.58 123

patch_mono-2		96.83 41	97.44 13	95.01 172	99.05 39	85.39 299	96.98 176	98.77 7	94.70 45	97.99 32	98.66 27	93.61 19	99.91 1	97.67 18	99.50 33	99.72 11

fmvsm_s_conf0.5_n		96.85 39	97.13 16	96.04 119	98.07 105	90.28 169	97.97 67	98.76 8	94.93 30	98.84 16	99.06 4	88.80 92	99.65 58	99.06 7	98.63 99	98.18 156

fmvsm_l_conf0.5_n		97.65 7	97.75 6	97.34 50	98.21 92	92.75 76	97.83 84	98.73 9	95.04 28	99.30 1	98.84 20	93.34 22	99.78 35	99.32 3	99.13 77	99.50 40

fmvsm_s_conf0.5_n_a		96.75 46	96.93 29	96.20 111	97.64 129	90.72 156	98.00 59	98.73 9	94.55 50	98.91 13	99.08 3	88.22 101	99.63 67	98.91 9	98.37 111	98.25 151

FC-MVSNet-test		93.94 127	93.57 120	95.04 169	95.48 243	91.45 124	98.12 48	98.71 11	93.37 91	90.23 228	96.70 162	87.66 110	97.85 281	91.49 171	90.39 269	95.83 247

UniMVSNet (Re)		93.31 150	92.55 164	95.61 143	95.39 248	93.34 64	97.39 139	98.71 11	93.14 103	90.10 237	94.83 260	87.71 109	98.03 254	91.67 169	83.99 337	95.46 270

fmvsm_l_conf0.5_n_a		97.63 8	97.76 5	97.26 57	98.25 86	92.59 82	97.81 88	98.68 13	94.93 30	99.24 3	98.87 15	93.52 20	99.79 33	99.32 3	99.21 69	99.40 54

FIs		94.09 121	93.70 116	95.27 159	95.70 233	92.03 101	98.10 49	98.68 13	93.36 93	90.39 225	96.70 162	87.63 112	97.94 271	92.25 151	90.50 268	95.84 246

WR-MVS_H		92.00 207	91.35 203	93.95 232	95.09 274	89.47 195	98.04 55	98.68 13	91.46 153	88.34 285	94.68 267	85.86 139	97.56 306	85.77 281	84.24 335	94.82 312

VPA-MVSNet		93.24 152	92.48 169	95.51 149	95.70 233	92.39 87	97.86 79	98.66 16	92.30 130	92.09 188	95.37 238	80.49 230	98.40 207	93.95 120	85.86 309	95.75 257

UniMVSNet_NR-MVSNet		93.37 148	92.67 158	95.47 154	95.34 254	92.83 74	97.17 162	98.58 17	92.98 112	90.13 233	95.80 215	88.37 100	97.85 281	91.71 166	83.93 338	95.73 259

CSCG		96.05 67	95.91 66	96.46 89	99.24 28	90.47 164	98.30 30	98.57 18	89.01 230	93.97 145	97.57 119	92.62 33	99.76 38	94.66 107	99.27 62	99.15 75

MSLP-MVS++		96.94 33	97.06 19	96.59 77	98.72 55	91.86 105	97.67 103	98.49 19	94.66 48	97.24 49	98.41 47	92.31 40	98.94 159	96.61 43	99.46 39	98.96 94

HyFIR lowres test		93.66 138	92.92 145	95.87 127	98.24 87	89.88 181	94.58 304	98.49 19	85.06 324	93.78 148	95.78 219	82.86 187	98.67 185	91.77 164	95.71 178	99.07 85

CHOSEN 1792x2688		94.15 116	93.51 126	96.06 117	98.27 83	89.38 200	95.18 292	98.48 21	85.60 314	93.76 149	97.11 142	83.15 178	99.61 69	91.33 174	98.72 96	99.19 71

PHI-MVS		96.77 44	96.46 56	97.71 39	98.40 75	94.07 46	98.21 43	98.45 22	89.86 204	97.11 54	98.01 83	92.52 35	99.69 52	96.03 64	99.53 27	99.36 60

fmvsm_s_conf0.1_n		96.58 54	96.77 40	96.01 123	96.67 182	90.25 170	97.91 75	98.38 23	94.48 53	98.84 16	99.14 1	88.06 103	99.62 68	98.82 11	98.60 101	98.15 159

PVSNet_BlendedMVS		94.06 122	93.92 112	94.47 203	98.27 83	89.46 197	96.73 195	98.36 24	90.17 197	94.36 134	95.24 244	88.02 104	99.58 77	93.44 131	90.72 264	94.36 332

PVSNet_Blended		94.87 101	94.56 98	95.81 130	98.27 83	89.46 197	95.47 278	98.36 24	88.84 238	94.36 134	96.09 204	88.02 104	99.58 77	93.44 131	98.18 119	98.40 143

3Dnovator	91.36 5	95.19 91	94.44 106	97.44 47	96.56 191	93.36 63	98.65 11	98.36 24	94.12 63	89.25 267	98.06 77	82.20 203	99.77 37	93.41 133	99.32 59	99.18 72

FOURS1					99.55 1	93.34 64	99.29 1	98.35 27	94.98 29	98.49 23

DPE-MVS		97.86 4	97.65 8	98.47 5	99.17 32	95.78 7	97.21 159	98.35 27	95.16 22	98.71 20	98.80 22	95.05 10	99.89 3	96.70 41	99.73 1	99.73 10
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
fmvsm_s_conf0.1_n_a		96.40 58	96.47 53	96.16 113	95.48 243	90.69 157	97.91 75	98.33 29	94.07 64	98.93 9	99.14 1	87.44 117	99.61 69	98.63 13	98.32 113	98.18 156

HFP-MVS		97.14 23	96.92 30	97.83 26	99.42 7	94.12 44	98.52 16	98.32 30	93.21 96	97.18 50	98.29 63	92.08 42	99.83 26	95.63 80	99.59 17	99.54 33

ACMMPR		97.07 26	96.84 33	97.79 30	99.44 6	93.88 50	98.52 16	98.31 31	93.21 96	97.15 51	98.33 57	91.35 57	99.86 8	95.63 80	99.59 17	99.62 18

test_fmvsmvis_n_1920		96.70 47	96.84 33	96.31 100	96.62 184	91.73 107	97.98 61	98.30 32	96.19 5	96.10 93	98.95 8	89.42 83	99.76 38	98.90 10	99.08 81	97.43 196

APDe-MVS		97.82 5	97.73 7	98.08 18	99.15 33	94.82 27	98.81 7	98.30 32	94.76 43	98.30 26	98.90 12	93.77 17	99.68 54	97.93 14	99.69 3	99.75 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test0726					99.45 3	95.36 13	98.31 29	98.29 34	94.92 32	98.99 7	98.92 10	95.08 8

MSP-MVS		97.59 10	97.54 10	97.73 36	99.40 11	93.77 54	98.53 15	98.29 34	95.55 13	98.56 22	97.81 99	93.90 15	99.65 58	96.62 42	99.21 69	99.77 2
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DVP-MVS++		98.06 1	97.99 1	98.28 9	98.67 58	95.39 11	99.29 1	98.28 36	94.78 41	98.93 9	98.87 15	96.04 2	99.86 8	97.45 26	99.58 21	99.59 22

test_0728_SECOND				98.51 4	99.45 3	95.93 5	98.21 43	98.28 36					99.86 8	97.52 22	99.67 6	99.75 6

CP-MVS		97.02 29	96.81 37	97.64 43	99.33 21	93.54 57	98.80 8	98.28 36	92.99 107	96.45 82	98.30 62	91.90 45	99.85 18	95.61 82	99.68 4	99.54 33

test_fmvsmconf0.1_n		97.09 24	97.06 19	97.19 62	95.67 235	92.21 94	97.95 70	98.27 39	95.78 10	98.40 25	99.00 6	89.99 78	99.78 35	99.06 7	99.41 49	99.59 22

SED-MVS		98.05 2	97.99 1	98.24 10	99.42 7	95.30 17	98.25 36	98.27 39	95.13 23	99.19 4	98.89 13	95.54 5	99.85 18	97.52 22	99.66 10	99.56 29

test_241102_TWO								98.27 39	95.13 23	98.93 9	98.89 13	94.99 11	99.85 18	97.52 22	99.65 12	99.74 8

test_241102_ONE					99.42 7	95.30 17		98.27 39	95.09 26	99.19 4	98.81 21	95.54 5	99.65 58

SF-MVS		97.39 15	97.13 16	98.17 15	99.02 42	95.28 19	98.23 40	98.27 39	92.37 129	98.27 27	98.65 29	93.33 23	99.72 45	96.49 47	99.52 28	99.51 37

SteuartSystems-ACMMP		97.62 9	97.53 11	97.87 24	98.39 77	94.25 38	98.43 24	98.27 39	95.34 17	98.11 28	98.56 31	94.53 12	99.71 46	96.57 45	99.62 15	99.65 15
Skip Steuart: Steuart Systems R&D Blog.
test_one_0601					99.32 22	95.20 20		98.25 45	95.13 23	98.48 24	98.87 15	95.16 7

PVSNet_Blended_VisFu		95.27 86	94.91 89	96.38 96	98.20 93	90.86 149	97.27 151	98.25 45	90.21 196	94.18 139	97.27 133	87.48 116	99.73 42	93.53 128	97.77 130	98.55 124

region2R		97.07 26	96.84 33	97.77 33	99.46 2	93.79 52	98.52 16	98.24 47	93.19 99	97.14 52	98.34 54	91.59 52	99.87 7	95.46 87	99.59 17	99.64 16

PS-CasMVS		91.55 223	90.84 224	93.69 248	94.96 278	88.28 234	97.84 83	98.24 47	91.46 153	88.04 295	95.80 215	79.67 246	97.48 314	87.02 261	84.54 332	95.31 282

DU-MVS		92.90 172	92.04 179	95.49 151	94.95 279	92.83 74	97.16 163	98.24 47	93.02 106	90.13 233	95.71 222	83.47 171	97.85 281	91.71 166	83.93 338	95.78 252

9.14			96.75 41		98.93 47		97.73 95	98.23 50	91.28 161	97.88 35	98.44 44	93.00 26	99.65 58	95.76 73	99.47 38

D2MVS		91.30 238	90.95 218	92.35 291	94.71 296	85.52 295	96.18 245	98.21 51	88.89 236	86.60 322	93.82 309	79.92 242	97.95 270	89.29 212	90.95 260	93.56 345

SDMVSNet		94.17 114	93.61 119	95.86 128	98.09 101	91.37 126	97.35 143	98.20 52	93.18 100	91.79 193	97.28 131	79.13 255	98.93 160	94.61 110	92.84 223	97.28 203

XVS		97.18 21	96.96 28	97.81 28	99.38 14	94.03 48	98.59 12	98.20 52	94.85 34	96.59 74	98.29 63	91.70 48	99.80 30	95.66 75	99.40 50	99.62 18

X-MVStestdata		91.71 214	89.67 273	97.81 28	99.38 14	94.03 48	98.59 12	98.20 52	94.85 34	96.59 74	32.69 399	91.70 48	99.80 30	95.66 75	99.40 50	99.62 18

ACMMP_NAP		97.20 20	96.86 31	98.23 11	99.09 34	95.16 22	97.60 115	98.19 55	92.82 118	97.93 34	98.74 26	91.60 51	99.86 8	96.26 50	99.52 28	99.67 13

CP-MVSNet		91.89 210	91.24 210	93.82 240	95.05 275	88.57 225	97.82 86	98.19 55	91.70 147	88.21 291	95.76 220	81.96 207	97.52 312	87.86 236	84.65 327	95.37 278

ZNCC-MVS		96.96 31	96.67 45	97.85 25	99.37 16	94.12 44	98.49 20	98.18 57	92.64 124	96.39 84	98.18 70	91.61 50	99.88 4	95.59 85	99.55 24	99.57 26

SMA-MVS		97.35 16	97.03 24	98.30 8	99.06 38	95.42 10	97.94 71	98.18 57	90.57 191	98.85 15	98.94 9	93.33 23	99.83 26	96.72 40	99.68 4	99.63 17
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
PEN-MVS		91.20 242	90.44 238	93.48 257	94.49 304	87.91 249	97.76 91	98.18 57	91.29 158	87.78 299	95.74 221	80.35 233	97.33 325	85.46 285	82.96 348	95.19 293

DELS-MVS		96.61 52	96.38 59	97.30 52	97.79 120	93.19 67	95.96 255	98.18 57	95.23 19	95.87 101	97.65 111	91.45 53	99.70 51	95.87 67	99.44 45	99.00 92
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
tfpnnormal		89.70 290	88.40 295	93.60 251	95.15 270	90.10 172	97.56 119	98.16 61	87.28 288	86.16 326	94.63 270	77.57 282	98.05 250	74.48 364	84.59 330	92.65 358

VNet		95.89 72	95.45 75	97.21 60	98.07 105	92.94 73	97.50 125	98.15 62	93.87 71	97.52 40	97.61 117	85.29 145	99.53 91	95.81 72	95.27 185	99.16 73

DeepPCF-MVS	93.97 1	96.61 52	97.09 18	95.15 163	98.09 101	86.63 279	96.00 253	98.15 62	95.43 14	97.95 33	98.56 31	93.40 21	99.36 114	96.77 38	99.48 37	99.45 47

SD-MVS		97.41 14	97.53 11	97.06 66	98.57 69	94.46 31	97.92 73	98.14 64	94.82 38	99.01 6	98.55 33	94.18 14	97.41 321	96.94 34	99.64 13	99.32 62
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
GST-MVS		96.85 39	96.52 51	97.82 27	99.36 18	94.14 43	98.29 31	98.13 65	92.72 121	96.70 66	98.06 77	91.35 57	99.86 8	94.83 101	99.28 61	99.47 46

UA-Net		95.95 71	95.53 72	97.20 61	97.67 125	92.98 72	97.65 106	98.13 65	94.81 39	96.61 72	98.35 51	88.87 90	99.51 96	90.36 189	97.35 142	99.11 81

QAPM		93.45 146	92.27 174	96.98 69	96.77 177	92.62 80	98.39 26	98.12 67	84.50 332	88.27 289	97.77 102	82.39 200	99.81 29	85.40 286	98.81 93	98.51 129

Vis-MVSNet		95.23 88	94.81 90	96.51 83	97.18 147	91.58 117	98.26 35	98.12 67	94.38 58	94.90 124	98.15 72	82.28 201	98.92 161	91.45 173	98.58 103	99.01 89
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVS	89.19 12	92.86 174	91.68 193	96.40 93	95.34 254	92.73 78	98.27 33	98.12 67	84.86 327	85.78 328	97.75 103	78.89 264	99.74 41	87.50 251	98.65 98	96.73 219

TranMVSNet+NR-MVSNet		92.50 183	91.63 194	95.14 164	94.76 291	92.07 99	97.53 123	98.11 70	92.90 116	89.56 255	96.12 200	83.16 177	97.60 304	89.30 211	83.20 347	95.75 257

CPTT-MVS		95.57 80	95.19 83	96.70 71	99.27 26	91.48 121	98.33 28	98.11 70	87.79 273	95.17 121	98.03 80	87.09 123	99.61 69	93.51 129	99.42 46	99.02 86

APD-MVS		96.95 32	96.60 47	98.01 19	99.03 41	94.93 26	97.72 98	98.10 72	91.50 151	98.01 31	98.32 59	92.33 38	99.58 77	94.85 100	99.51 31	99.53 36
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mPP-MVS		96.86 37	96.60 47	97.64 43	99.40 11	93.44 59	98.50 19	98.09 73	93.27 95	95.95 100	98.33 57	91.04 64	99.88 4	95.20 92	99.57 23	99.60 21

ZD-MVS					99.05 39	94.59 29		98.08 74	89.22 224	97.03 57	98.10 73	92.52 35	99.65 58	94.58 111	99.31 60

MTGPA								98.08 74

MTAPA		97.08 25	96.78 39	97.97 22	99.37 16	94.42 33	97.24 153	98.08 74	95.07 27	96.11 92	98.59 30	90.88 68	99.90 2	96.18 59	99.50 33	99.58 25

CNVR-MVS		97.68 6	97.44 13	98.37 7	98.90 50	95.86 6	97.27 151	98.08 74	95.81 9	97.87 36	98.31 60	94.26 13	99.68 54	97.02 33	99.49 36	99.57 26

DP-MVS Recon		95.68 76	95.12 86	97.37 49	99.19 31	94.19 40	97.03 169	98.08 74	88.35 256	95.09 123	97.65 111	89.97 79	99.48 101	92.08 158	98.59 102	98.44 140

SR-MVS		97.01 30	96.86 31	97.47 46	99.09 34	93.27 66	97.98 61	98.07 79	93.75 74	97.45 42	98.48 41	91.43 55	99.59 74	96.22 53	99.27 62	99.54 33

MCST-MVS		97.18 21	96.84 33	98.20 14	99.30 24	95.35 15	97.12 166	98.07 79	93.54 83	96.08 94	97.69 106	93.86 16	99.71 46	96.50 46	99.39 52	99.55 32

NR-MVSNet		92.34 192	91.27 209	95.53 148	94.95 279	93.05 70	97.39 139	98.07 79	92.65 123	84.46 339	95.71 222	85.00 149	97.77 290	89.71 200	83.52 344	95.78 252

MP-MVS-pluss		96.70 47	96.27 61	97.98 21	99.23 30	94.71 28	96.96 178	98.06 82	90.67 182	95.55 113	98.78 25	91.07 63	99.86 8	96.58 44	99.55 24	99.38 58
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
APD-MVS_3200maxsize		96.81 42	96.71 44	97.12 64	99.01 45	92.31 91	97.98 61	98.06 82	93.11 104	97.44 43	98.55 33	90.93 66	99.55 87	96.06 60	99.25 66	99.51 37

MP-MVS		96.77 44	96.45 57	97.72 37	99.39 13	93.80 51	98.41 25	98.06 82	93.37 91	95.54 115	98.34 54	90.59 72	99.88 4	94.83 101	99.54 26	99.49 42
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS_fast		96.51 55	96.27 61	97.22 59	99.32 22	92.74 77	98.74 9	98.06 82	90.57 191	96.77 63	98.35 51	90.21 75	99.53 91	94.80 104	99.63 14	99.38 58

HPM-MVS		96.69 49	96.45 57	97.40 48	99.36 18	93.11 69	98.87 6	98.06 82	91.17 166	96.40 83	97.99 84	90.99 65	99.58 77	95.61 82	99.61 16	99.49 42
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
sss		94.51 107	93.80 114	96.64 72	97.07 154	91.97 103	96.32 234	98.06 82	88.94 234	94.50 132	96.78 157	84.60 153	99.27 122	91.90 159	96.02 169	98.68 120

DeepC-MVS	93.07 3	96.06 66	95.66 70	97.29 53	97.96 109	93.17 68	97.30 149	98.06 82	93.92 69	93.38 158	98.66 27	86.83 125	99.73 42	95.60 84	99.22 68	98.96 94
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
NCCC		97.30 18	97.03 24	98.11 17	98.77 53	95.06 24	97.34 144	98.04 89	95.96 6	97.09 55	97.88 92	93.18 25	99.71 46	95.84 71	99.17 73	99.56 29

DeepC-MVS_fast	93.89 2	96.93 34	96.64 46	97.78 31	98.64 64	94.30 35	97.41 134	98.04 89	94.81 39	96.59 74	98.37 49	91.24 59	99.64 66	95.16 93	99.52 28	99.42 53
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS-dyc-post		96.88 36	96.80 38	97.11 65	99.02 42	92.34 89	97.98 61	98.03 91	93.52 85	97.43 45	98.51 36	91.40 56	99.56 85	96.05 61	99.26 64	99.43 51

RE-MVS-def			96.72 43		99.02 42	92.34 89	97.98 61	98.03 91	93.52 85	97.43 45	98.51 36	90.71 70		96.05 61	99.26 64	99.43 51

RPMNet		88.98 295	87.05 309	94.77 191	94.45 306	87.19 264	90.23 376	98.03 91	77.87 378	92.40 175	87.55 381	80.17 237	99.51 96	68.84 382	93.95 212	97.60 190

save fliter					98.91 49	94.28 36	97.02 171	98.02 94	95.35 16

TEST9					98.70 56	94.19 40	96.41 223	98.02 94	88.17 260	96.03 95	97.56 121	92.74 30	99.59 74

train_agg		96.30 62	95.83 69	97.72 37	98.70 56	94.19 40	96.41 223	98.02 94	88.58 247	96.03 95	97.56 121	92.73 31	99.59 74	95.04 95	99.37 56	99.39 56

test_8					98.67 58	94.06 47	96.37 230	98.01 97	88.58 247	95.98 99	97.55 123	92.73 31	99.58 77

agg_prior					98.67 58	93.79 52		98.00 98		95.68 109			99.57 84

test_prior				97.23 58	98.67 58	92.99 71		98.00 98					99.41 109			99.29 63

WR-MVS		92.34 192	91.53 198	94.77 191	95.13 272	90.83 151	96.40 227	97.98 100	91.88 144	89.29 264	95.54 233	82.50 196	97.80 286	89.79 199	85.27 318	95.69 261

HPM-MVS++		97.34 17	96.97 27	98.47 5	99.08 36	96.16 4	97.55 122	97.97 101	95.59 11	96.61 72	97.89 90	92.57 34	99.84 23	95.95 66	99.51 31	99.40 54

CANet		96.39 59	96.02 64	97.50 45	97.62 131	93.38 61	97.02 171	97.96 102	95.42 15	94.86 125	97.81 99	87.38 119	99.82 28	96.88 36	99.20 71	99.29 63

114514_t		93.95 126	93.06 141	96.63 74	99.07 37	91.61 114	97.46 133	97.96 102	77.99 376	93.00 166	97.57 119	86.14 137	99.33 115	89.22 215	99.15 75	98.94 97

IU-MVS					99.42 7	95.39 11		97.94 104	90.40 195	98.94 8				97.41 29	99.66 10	99.74 8

MSC_two_6792asdad				98.86 1	98.67 58	96.94 1		97.93 105					99.86 8	97.68 16	99.67 6	99.77 2

No_MVS				98.86 1	98.67 58	96.94 1		97.93 105					99.86 8	97.68 16	99.67 6	99.77 2

Anonymous20231211		90.63 265	89.42 279	94.27 216	98.24 87	89.19 212	98.05 54	97.89 107	79.95 368	88.25 290	94.96 252	72.56 322	98.13 232	89.70 201	85.14 320	95.49 265

原ACMM1				96.38 96	98.59 66	91.09 142		97.89 107	87.41 284	95.22 120	97.68 107	90.25 74	99.54 89	87.95 235	99.12 79	98.49 132

CDPH-MVS		95.97 70	95.38 78	97.77 33	98.93 47	94.44 32	96.35 231	97.88 109	86.98 292	96.65 70	97.89 90	91.99 44	99.47 102	92.26 149	99.46 39	99.39 56

test11								97.88 109

EIA-MVS		95.53 81	95.47 74	95.71 138	97.06 157	89.63 186	97.82 86	97.87 111	93.57 79	93.92 146	95.04 250	90.61 71	98.95 158	94.62 109	98.68 97	98.54 125

CS-MVS		96.86 37	97.06 19	96.26 106	98.16 98	91.16 140	99.09 3	97.87 111	95.30 18	97.06 56	98.03 80	91.72 46	98.71 182	97.10 31	99.17 73	98.90 102

无先验							95.79 263	97.87 111	83.87 340				99.65 58	87.68 245		98.89 105

3Dnovator+	91.43 4	95.40 82	94.48 104	98.16 16	96.90 166	95.34 16	98.48 21	97.87 111	94.65 49	88.53 282	98.02 82	83.69 167	99.71 46	93.18 136	98.96 88	99.44 49

VPNet		92.23 200	91.31 206	94.99 173	95.56 239	90.96 145	97.22 158	97.86 115	92.96 114	90.96 216	96.62 177	75.06 304	98.20 225	91.90 159	83.65 343	95.80 250

test_vis1_n_1920		94.17 114	94.58 97	92.91 277	97.42 141	82.02 338	97.83 84	97.85 116	94.68 46	98.10 29	98.49 38	70.15 337	99.32 117	97.91 15	98.82 92	97.40 197

DVP-MVS		97.91 3	97.81 4	98.22 13	99.45 3	95.36 13	98.21 43	97.85 116	94.92 32	98.73 18	98.87 15	95.08 8	99.84 23	97.52 22	99.67 6	99.48 44
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
TSAR-MVS + MP.		97.42 13	97.33 15	97.69 40	99.25 27	94.24 39	98.07 52	97.85 116	93.72 75	98.57 21	98.35 51	93.69 18	99.40 110	97.06 32	99.46 39	99.44 49
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CS-MVS-test		96.89 35	97.04 23	96.45 90	98.29 82	91.66 113	99.03 4	97.85 116	95.84 7	96.90 59	97.97 86	91.24 59	98.75 176	96.92 35	99.33 58	98.94 97

test_fmvsmconf0.01_n		96.15 65	95.85 68	97.03 67	92.66 353	91.83 106	97.97 67	97.84 120	95.57 12	97.53 39	99.00 6	84.20 161	99.76 38	98.82 11	99.08 81	99.48 44

AdaColmap		94.34 110	93.68 117	96.31 100	98.59 66	91.68 112	96.59 214	97.81 121	89.87 203	92.15 184	97.06 145	83.62 170	99.54 89	89.34 210	98.07 122	97.70 183

ETV-MVS		96.02 68	95.89 67	96.40 93	97.16 148	92.44 86	97.47 131	97.77 122	94.55 50	96.48 79	94.51 274	91.23 61	98.92 161	95.65 78	98.19 118	97.82 178

新几何1				97.32 51	98.60 65	93.59 56		97.75 123	81.58 359	95.75 106	97.85 96	90.04 77	99.67 56	86.50 267	99.13 77	98.69 119

旧先验1					98.38 78	93.38 61		97.75 123			98.09 75	92.30 41			99.01 86	99.16 73

EC-MVSNet		96.42 57	96.47 53	96.26 106	97.01 162	91.52 119	98.89 5	97.75 123	94.42 55	96.64 71	97.68 107	89.32 84	98.60 192	97.45 26	99.11 80	98.67 121

EI-MVSNet-Vis-set		96.51 55	96.47 53	96.63 74	98.24 87	91.20 135	96.89 182	97.73 126	94.74 44	96.49 78	98.49 38	90.88 68	99.58 77	96.44 48	98.32 113	99.13 77

PAPM_NR		95.01 93	94.59 96	96.26 106	98.89 51	90.68 159	97.24 153	97.73 126	91.80 145	92.93 171	96.62 177	89.13 87	99.14 135	89.21 216	97.78 129	98.97 93

Anonymous20240529		91.98 208	90.73 229	95.73 136	98.14 99	89.40 199	97.99 60	97.72 128	79.63 370	93.54 153	97.41 127	69.94 339	99.56 85	91.04 180	91.11 256	98.22 153

CHOSEN 280x420		93.12 159	92.72 157	94.34 211	96.71 181	87.27 260	90.29 375	97.72 128	86.61 299	91.34 205	95.29 240	84.29 160	98.41 206	93.25 135	98.94 89	97.35 200

EI-MVSNet-UG-set		96.34 61	96.30 60	96.47 87	98.20 93	90.93 147	96.86 184	97.72 128	94.67 47	96.16 91	98.46 42	90.43 73	99.58 77	96.23 52	97.96 125	98.90 102

LS3D		93.57 142	92.61 162	96.47 87	97.59 135	91.61 114	97.67 103	97.72 128	85.17 322	90.29 227	98.34 54	84.60 153	99.73 42	83.85 306	98.27 115	98.06 167

PAPR		94.18 113	93.42 133	96.48 86	97.64 129	91.42 125	95.55 273	97.71 132	88.99 231	92.34 180	95.82 214	89.19 85	99.11 138	86.14 273	97.38 140	98.90 102

UGNet		94.04 124	93.28 136	96.31 100	96.85 168	91.19 136	97.88 78	97.68 133	94.40 56	93.00 166	96.18 196	73.39 319	99.61 69	91.72 165	98.46 108	98.13 160
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
testdata				95.46 155	98.18 97	88.90 218		97.66 134	82.73 350	97.03 57	98.07 76	90.06 76	98.85 166	89.67 202	98.98 87	98.64 122

test12				97.65 41	98.46 70	94.26 37		97.66 134		95.52 116		90.89 67	99.46 103		99.25 66	99.22 70

DTE-MVSNet		90.56 266	89.75 271	93.01 273	93.95 320	87.25 261	97.64 110	97.65 136	90.74 177	87.12 311	95.68 225	79.97 241	97.00 337	83.33 307	81.66 354	94.78 319

TAPA-MVS	90.10 7	92.30 195	91.22 212	95.56 145	98.33 80	89.60 188	96.79 190	97.65 136	81.83 356	91.52 200	97.23 136	87.94 106	98.91 163	71.31 377	98.37 111	98.17 158
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sd_testset		93.10 160	92.45 170	95.05 168	98.09 101	89.21 209	96.89 182	97.64 138	93.18 100	91.79 193	97.28 131	75.35 303	98.65 187	88.99 221	92.84 223	97.28 203

test_cas_vis1_n_1920		94.48 108	94.55 101	94.28 215	96.78 175	86.45 281	97.63 112	97.64 138	93.32 94	97.68 38	98.36 50	73.75 317	99.08 144	96.73 39	99.05 83	97.31 202

cdsmvs_eth3d_5k		23.24 369	30.99 371	0.00 387	0.00 410	0.00 412	0.00 398	97.63 140	0.00 405	0.00 406	96.88 155	84.38 157	0.00 406	0.00 405	0.00 404	0.00 402

DPM-MVS		95.69 75	94.92 88	98.01 19	98.08 104	95.71 9	95.27 288	97.62 141	90.43 194	95.55 113	97.07 144	91.72 46	99.50 99	89.62 204	98.94 89	98.82 111

canonicalmvs		96.02 68	95.45 75	97.75 35	97.59 135	95.15 23	98.28 32	97.60 142	94.52 52	96.27 88	96.12 200	87.65 111	99.18 130	96.20 58	94.82 193	98.91 101

test222					98.24 87	92.21 94	95.33 283	97.60 142	79.22 372	95.25 118	97.84 98	88.80 92			99.15 75	98.72 116

cascas		91.20 242	90.08 255	94.58 200	94.97 277	89.16 213	93.65 341	97.59 144	79.90 369	89.40 259	92.92 330	75.36 302	98.36 213	92.14 154	94.75 195	96.23 229

h-mvs33		94.15 116	93.52 125	96.04 119	97.81 119	90.22 171	97.62 114	97.58 145	95.19 20	96.74 64	97.45 124	83.67 168	99.61 69	95.85 69	79.73 361	98.29 150

MVSFormer		95.37 83	95.16 84	95.99 124	96.34 206	91.21 133	98.22 41	97.57 146	91.42 155	96.22 89	97.32 129	86.20 135	97.92 275	94.07 117	99.05 83	98.85 108

test_djsdf		93.07 163	92.76 152	94.00 227	93.49 336	88.70 222	98.22 41	97.57 146	91.42 155	90.08 239	95.55 232	82.85 188	97.92 275	94.07 117	91.58 244	95.40 275

OMC-MVS		95.09 92	94.70 94	96.25 109	98.46 70	91.28 129	96.43 221	97.57 146	92.04 140	94.77 127	97.96 87	87.01 124	99.09 142	91.31 175	96.77 156	98.36 147

PS-MVSNAJss		93.74 136	93.51 126	94.44 204	93.91 322	89.28 207	97.75 92	97.56 149	92.50 126	89.94 242	96.54 180	88.65 95	98.18 228	93.83 126	90.90 261	95.86 243

casdiffmvs_mvg		95.81 74	95.57 71	96.51 83	96.87 167	91.49 120	97.50 125	97.56 149	93.99 67	95.13 122	97.92 89	87.89 107	98.78 171	95.97 65	97.33 143	99.26 67
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
jajsoiax		92.42 187	91.89 186	94.03 226	93.33 342	88.50 229	97.73 95	97.53 151	92.00 142	88.85 274	96.50 182	75.62 301	98.11 238	93.88 124	91.56 245	95.48 266

mvs_tets		92.31 194	91.76 188	93.94 234	93.41 339	88.29 233	97.63 112	97.53 151	92.04 140	88.76 277	96.45 184	74.62 309	98.09 242	93.91 122	91.48 247	95.45 271

dcpmvs_2		96.37 60	97.05 22	94.31 213	98.96 46	84.11 317	97.56 119	97.51 153	93.92 69	97.43 45	98.52 35	92.75 29	99.32 117	97.32 30	99.50 33	99.51 37

HQP_MVS		93.78 135	93.43 131	94.82 184	96.21 210	89.99 176	97.74 93	97.51 153	94.85 34	91.34 205	96.64 168	81.32 217	98.60 192	93.02 142	92.23 232	95.86 243

plane_prior5								97.51 153					98.60 192	93.02 142	92.23 232	95.86 243

PS-MVSNAJ		95.37 83	95.33 80	95.49 151	97.35 142	90.66 160	95.31 285	97.48 156	93.85 72	96.51 77	95.70 224	88.65 95	99.65 58	94.80 104	98.27 115	96.17 233

API-MVS		94.84 102	94.49 103	95.90 126	97.90 115	92.00 102	97.80 89	97.48 156	89.19 225	94.81 126	96.71 160	88.84 91	99.17 131	88.91 223	98.76 95	96.53 222

MG-MVS		95.61 78	95.38 78	96.31 100	98.42 73	90.53 162	96.04 250	97.48 156	93.47 87	95.67 110	98.10 73	89.17 86	99.25 123	91.27 176	98.77 94	99.13 77

MAR-MVS		94.22 112	93.46 128	96.51 83	98.00 108	92.19 97	97.67 103	97.47 159	88.13 263	93.00 166	95.84 212	84.86 151	99.51 96	87.99 234	98.17 120	97.83 177
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
CLD-MVS		92.98 167	92.53 166	94.32 212	96.12 220	89.20 210	95.28 286	97.47 159	92.66 122	89.90 243	95.62 228	80.58 228	98.40 207	92.73 147	92.40 230	95.38 277
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_ETH3D		91.34 236	90.22 251	94.68 194	94.86 287	87.86 250	97.23 157	97.46 161	87.99 264	89.90 243	96.92 153	66.35 358	98.23 222	90.30 190	90.99 259	97.96 168

nrg030		94.05 123	93.31 135	96.27 105	95.22 265	94.59 29	98.34 27	97.46 161	92.93 115	91.21 214	96.64 168	87.23 122	98.22 223	94.99 98	85.80 310	95.98 242

XVG-OURS		93.72 137	93.35 134	94.80 189	97.07 154	88.61 223	94.79 299	97.46 161	91.97 143	93.99 143	97.86 95	81.74 212	98.88 165	92.64 148	92.67 228	96.92 214

LPG-MVS_test		92.94 170	92.56 163	94.10 221	96.16 215	88.26 235	97.65 106	97.46 161	91.29 158	90.12 235	97.16 139	79.05 257	98.73 178	92.25 151	91.89 240	95.31 282

LGP-MVS_train				94.10 221	96.16 215	88.26 235		97.46 161	91.29 158	90.12 235	97.16 139	79.05 257	98.73 178	92.25 151	91.89 240	95.31 282

MVS		91.71 214	90.44 238	95.51 149	95.20 267	91.59 116	96.04 250	97.45 166	73.44 384	87.36 308	95.60 229	85.42 144	99.10 139	85.97 278	97.46 135	95.83 247

XVG-OURS-SEG-HR		93.86 131	93.55 121	94.81 186	97.06 157	88.53 228	95.28 286	97.45 166	91.68 148	94.08 142	97.68 107	82.41 199	98.90 164	93.84 125	92.47 229	96.98 210

baseline		95.58 79	95.42 77	96.08 115	96.78 175	90.41 167	97.16 163	97.45 166	93.69 78	95.65 111	97.85 96	87.29 120	98.68 184	95.66 75	97.25 147	99.13 77

ab-mvs		93.57 142	92.55 164	96.64 72	97.28 143	91.96 104	95.40 280	97.45 166	89.81 208	93.22 164	96.28 192	79.62 248	99.46 103	90.74 184	93.11 220	98.50 130

xiu_mvs_v2_base		95.32 85	95.29 81	95.40 156	97.22 144	90.50 163	95.44 279	97.44 170	93.70 77	96.46 81	96.18 196	88.59 98	99.53 91	94.79 106	97.81 128	96.17 233

1314		92.81 178	92.03 180	95.14 164	95.33 257	89.52 194	96.04 250	97.44 170	87.72 277	86.25 325	95.33 239	83.84 165	98.79 170	89.26 213	97.05 152	97.11 208

casdiffmvs		95.64 77	95.49 73	96.08 115	96.76 180	90.45 165	97.29 150	97.44 170	94.00 66	95.46 117	97.98 85	87.52 115	98.73 178	95.64 79	97.33 143	99.08 83
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
XXY-MVS		92.16 202	91.23 211	94.95 178	94.75 293	90.94 146	97.47 131	97.43 173	89.14 226	88.90 271	96.43 185	79.71 245	98.24 221	89.56 205	87.68 293	95.67 263

anonymousdsp		92.16 202	91.55 197	93.97 230	92.58 355	89.55 191	97.51 124	97.42 174	89.42 219	88.40 284	94.84 259	80.66 226	97.88 280	91.87 161	91.28 252	94.48 327

Effi-MVS+		94.93 98	94.45 105	96.36 98	96.61 185	91.47 122	96.41 223	97.41 175	91.02 171	94.50 132	95.92 208	87.53 114	98.78 171	93.89 123	96.81 155	98.84 110

HQP3-MVS								97.39 176							92.10 237

HQP-MVS		93.19 155	92.74 155	94.54 202	95.86 226	89.33 203	96.65 205	97.39 176	93.55 80	90.14 229	95.87 210	80.95 220	98.50 200	92.13 155	92.10 237	95.78 252

PLC	91.00 6	94.11 120	93.43 131	96.13 114	98.58 68	91.15 141	96.69 201	97.39 176	87.29 287	91.37 204	96.71 160	88.39 99	99.52 95	87.33 254	97.13 151	97.73 181
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7n		90.76 259	89.86 264	93.45 259	93.54 333	87.60 256	97.70 102	97.37 179	88.85 237	87.65 301	94.08 301	81.08 219	98.10 239	84.68 294	83.79 342	94.66 324

UnsupCasMVSNet_eth		85.99 328	84.45 332	90.62 332	89.97 373	82.40 335	93.62 342	97.37 179	89.86 204	78.59 373	92.37 339	65.25 365	95.35 366	82.27 320	70.75 382	94.10 338

ACMM	89.79 8	92.96 168	92.50 168	94.35 209	96.30 208	88.71 221	97.58 117	97.36 181	91.40 157	90.53 221	96.65 167	79.77 244	98.75 176	91.24 177	91.64 242	95.59 264
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
xiu_mvs_v1_base_debu		95.01 93	94.76 91	95.75 133	96.58 188	91.71 109	96.25 239	97.35 182	92.99 107	96.70 66	96.63 174	82.67 191	99.44 106	96.22 53	97.46 135	96.11 238

xiu_mvs_v1_base		95.01 93	94.76 91	95.75 133	96.58 188	91.71 109	96.25 239	97.35 182	92.99 107	96.70 66	96.63 174	82.67 191	99.44 106	96.22 53	97.46 135	96.11 238

xiu_mvs_v1_base_debi		95.01 93	94.76 91	95.75 133	96.58 188	91.71 109	96.25 239	97.35 182	92.99 107	96.70 66	96.63 174	82.67 191	99.44 106	96.22 53	97.46 135	96.11 238

diffmvs		95.25 87	95.13 85	95.63 141	96.43 202	89.34 202	95.99 254	97.35 182	92.83 117	96.31 85	97.37 128	86.44 130	98.67 185	96.26 50	97.19 149	98.87 107
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
WTY-MVS		94.71 106	94.02 110	96.79 70	97.71 124	92.05 100	96.59 214	97.35 182	90.61 188	94.64 129	96.93 150	86.41 131	99.39 111	91.20 178	94.71 197	98.94 97

F-COLMAP		93.58 141	92.98 143	95.37 157	98.40 75	88.98 216	97.18 161	97.29 187	87.75 276	90.49 222	97.10 143	85.21 146	99.50 99	86.70 264	96.72 159	97.63 185

XVG-ACMP-BASELINE		90.93 255	90.21 252	93.09 271	94.31 312	85.89 290	95.33 283	97.26 188	91.06 170	89.38 260	95.44 237	68.61 344	98.60 192	89.46 207	91.05 257	94.79 317

PCF-MVS	89.48 11	91.56 222	89.95 261	96.36 98	96.60 186	92.52 84	92.51 361	97.26 188	79.41 371	88.90 271	96.56 179	84.04 164	99.55 87	77.01 355	97.30 145	97.01 209
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ACMP	89.59 10	92.62 182	92.14 177	94.05 224	96.40 203	88.20 238	97.36 142	97.25 190	91.52 150	88.30 287	96.64 168	78.46 269	98.72 181	91.86 162	91.48 247	95.23 289
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OPM-MVS		93.28 151	92.76 152	94.82 184	94.63 299	90.77 154	96.65 205	97.18 191	93.72 75	91.68 196	97.26 134	79.33 252	98.63 189	92.13 155	92.28 231	95.07 295
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PatchMatch-RL		92.90 172	92.02 181	95.56 145	98.19 95	90.80 152	95.27 288	97.18 191	87.96 265	91.86 192	95.68 225	80.44 231	98.99 156	84.01 302	97.54 134	96.89 215

alignmvs		95.87 73	95.23 82	97.78 31	97.56 138	95.19 21	97.86 79	97.17 193	94.39 57	96.47 80	96.40 187	85.89 138	99.20 127	96.21 57	95.11 189	98.95 96

MVS_Test		94.89 100	94.62 95	95.68 139	96.83 171	89.55 191	96.70 199	97.17 193	91.17 166	95.60 112	96.11 203	87.87 108	98.76 175	93.01 144	97.17 150	98.72 116

Fast-Effi-MVS+		93.46 145	92.75 154	95.59 144	96.77 177	90.03 173	96.81 189	97.13 195	88.19 259	91.30 208	94.27 290	86.21 134	98.63 189	87.66 246	96.46 166	98.12 161

EI-MVSNet		93.03 165	92.88 147	93.48 257	95.77 231	86.98 269	96.44 219	97.12 196	90.66 184	91.30 208	97.64 114	86.56 127	98.05 250	89.91 195	90.55 266	95.41 272

MVSTER		93.20 154	92.81 151	94.37 208	96.56 191	89.59 189	97.06 168	97.12 196	91.24 162	91.30 208	95.96 206	82.02 206	98.05 250	93.48 130	90.55 266	95.47 269

test_yl		94.78 104	94.23 108	96.43 91	97.74 122	91.22 131	96.85 185	97.10 198	91.23 163	95.71 107	96.93 150	84.30 158	99.31 119	93.10 137	95.12 187	98.75 113

DCV-MVSNet		94.78 104	94.23 108	96.43 91	97.74 122	91.22 131	96.85 185	97.10 198	91.23 163	95.71 107	96.93 150	84.30 158	99.31 119	93.10 137	95.12 187	98.75 113

LTVRE_ROB	88.41 13	90.99 251	89.92 263	94.19 217	96.18 213	89.55 191	96.31 235	97.09 200	87.88 268	85.67 329	95.91 209	78.79 265	98.57 196	81.50 323	89.98 272	94.44 330
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
test_fmvs1_n		92.73 180	92.88 147	92.29 294	96.08 223	81.05 346	97.98 61	97.08 201	90.72 179	96.79 62	98.18 70	63.07 369	98.45 204	97.62 20	98.42 110	97.36 198

v10		91.04 249	90.23 249	93.49 256	94.12 316	88.16 241	97.32 147	97.08 201	88.26 258	88.29 288	94.22 295	82.17 204	97.97 262	86.45 268	84.12 336	94.33 333

v144192		91.06 248	90.28 245	93.39 260	93.66 331	87.23 263	96.83 188	97.07 203	87.43 283	89.69 250	94.28 289	81.48 215	98.00 257	87.18 258	84.92 326	94.93 303

v1192		91.07 247	90.23 249	93.58 253	93.70 328	87.82 252	96.73 195	97.07 203	87.77 274	89.58 253	94.32 287	80.90 224	97.97 262	86.52 266	85.48 313	94.95 299

v8		91.29 239	90.53 237	93.57 254	94.15 315	88.12 242	97.34 144	97.06 205	88.99 231	88.32 286	94.26 292	83.08 180	98.01 256	87.62 248	83.92 340	94.57 326

mvs_anonymous		93.82 133	93.74 115	94.06 223	96.44 201	85.41 297	95.81 262	97.05 206	89.85 206	90.09 238	96.36 189	87.44 117	97.75 291	93.97 119	96.69 160	99.02 86

IterMVS-LS		92.29 196	91.94 184	93.34 262	96.25 209	86.97 270	96.57 217	97.05 206	90.67 182	89.50 258	94.80 262	86.59 126	97.64 299	89.91 195	86.11 308	95.40 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		90.85 257	90.03 260	93.29 264	93.55 332	86.96 271	96.74 194	97.04 208	87.36 285	89.52 257	94.34 284	80.23 236	97.97 262	86.27 269	85.21 319	94.94 301

CDS-MVSNet		94.14 119	93.54 122	95.93 125	96.18 213	91.46 123	96.33 233	97.04 208	88.97 233	93.56 151	96.51 181	87.55 113	97.89 279	89.80 198	95.95 171	98.44 140
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v1144		91.37 233	90.60 233	93.68 249	93.89 323	88.23 237	96.84 187	97.03 210	88.37 255	89.69 250	94.39 281	82.04 205	97.98 259	87.80 238	85.37 315	94.84 309

v1240		90.70 263	89.85 265	93.23 266	93.51 335	86.80 272	96.61 211	97.02 211	87.16 290	89.58 253	94.31 288	79.55 249	97.98 259	85.52 284	85.44 314	94.90 306

EPP-MVSNet		95.22 89	95.04 87	95.76 131	97.49 139	89.56 190	98.67 10	97.00 212	90.69 180	94.24 137	97.62 116	89.79 81	98.81 169	93.39 134	96.49 164	98.92 100

V42		91.58 221	90.87 220	93.73 244	94.05 319	88.50 229	97.32 147	96.97 213	88.80 243	89.71 248	94.33 285	82.54 195	98.05 250	89.01 220	85.07 322	94.64 325

test_fmvs1		93.21 153	93.53 123	92.25 296	96.55 193	81.20 345	97.40 138	96.96 214	90.68 181	96.80 61	98.04 79	69.25 341	98.40 207	97.58 21	98.50 104	97.16 207

FMVSNet2		91.31 237	90.08 255	94.99 173	96.51 196	92.21 94	97.41 134	96.95 215	88.82 240	88.62 279	94.75 264	73.87 313	97.42 320	85.20 289	88.55 287	95.35 279

ACMH	87.59 16	90.53 267	89.42 279	93.87 238	96.21 210	87.92 247	97.24 153	96.94 216	88.45 253	83.91 349	96.27 193	71.92 323	98.62 191	84.43 297	89.43 278	95.05 297
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		91.35 234	90.27 246	94.59 196	96.51 196	91.18 137	97.50 125	96.93 217	88.82 240	89.35 261	94.51 274	73.87 313	97.29 327	86.12 274	88.82 282	95.31 282

test1		91.35 234	90.27 246	94.59 196	96.51 196	91.18 137	97.50 125	96.93 217	88.82 240	89.35 261	94.51 274	73.87 313	97.29 327	86.12 274	88.82 282	95.31 282

FMVSNet3		91.78 212	90.69 231	95.03 171	96.53 195	92.27 93	97.02 171	96.93 217	89.79 209	89.35 261	94.65 269	77.01 285	97.47 315	86.12 274	88.82 282	95.35 279

FMVSNet1		89.88 285	88.31 296	94.59 196	95.41 247	91.18 137	97.50 125	96.93 217	86.62 298	87.41 306	94.51 274	65.94 362	97.29 327	83.04 310	87.43 296	95.31 282

GeoE		93.89 129	93.28 136	95.72 137	96.96 165	89.75 184	98.24 39	96.92 221	89.47 217	92.12 186	97.21 137	84.42 156	98.39 211	87.71 241	96.50 163	99.01 89

miper_enhance_ethall		91.54 224	91.01 217	93.15 269	95.35 253	87.07 268	93.97 327	96.90 222	86.79 296	89.17 268	93.43 325	86.55 128	97.64 299	89.97 194	86.93 300	94.74 321

eth_miper_zixun_eth		91.02 250	90.59 234	92.34 293	95.33 257	84.35 313	94.10 324	96.90 222	88.56 249	88.84 275	94.33 285	84.08 163	97.60 304	88.77 226	84.37 334	95.06 296

TAMVS		94.01 125	93.46 128	95.64 140	96.16 215	90.45 165	96.71 198	96.89 224	89.27 223	93.46 156	96.92 153	87.29 120	97.94 271	88.70 227	95.74 176	98.53 126

miper_ehance_all_eth		91.59 219	91.13 215	92.97 275	95.55 240	86.57 280	94.47 308	96.88 225	87.77 274	88.88 273	94.01 302	86.22 133	97.54 308	89.49 206	86.93 300	94.79 317

v2v482		91.59 219	90.85 223	93.80 241	93.87 324	88.17 240	96.94 179	96.88 225	89.54 214	89.53 256	94.90 256	81.70 213	98.02 255	89.25 214	85.04 324	95.20 290

CNLPA		94.28 111	93.53 123	96.52 80	98.38 78	92.55 83	96.59 214	96.88 225	90.13 200	91.91 190	97.24 135	85.21 146	99.09 142	87.64 247	97.83 127	97.92 170

PAPM		91.52 225	90.30 244	95.20 161	95.30 260	89.83 182	93.38 347	96.85 228	86.26 305	88.59 280	95.80 215	84.88 150	98.15 230	75.67 360	95.93 172	97.63 185

c3_l		91.38 231	90.89 219	92.88 279	95.58 238	86.30 284	94.68 301	96.84 229	88.17 260	88.83 276	94.23 293	85.65 142	97.47 315	89.36 209	84.63 328	94.89 307

pm-mvs1		90.72 262	89.65 275	93.96 231	94.29 313	89.63 186	97.79 90	96.82 230	89.07 227	86.12 327	95.48 236	78.61 267	97.78 288	86.97 262	81.67 353	94.46 328

test_vis1_n		92.37 190	92.26 175	92.72 284	94.75 293	82.64 330	98.02 56	96.80 231	91.18 165	97.77 37	97.93 88	58.02 377	98.29 219	97.63 19	98.21 117	97.23 206

CMPMVS	62.92 21	85.62 332	84.92 329	87.74 353	89.14 378	73.12 383	94.17 322	96.80 231	73.98 382	73.65 381	94.93 254	66.36 357	97.61 303	83.95 304	91.28 252	92.48 362
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MS-PatchMatch		90.27 274	89.77 269	91.78 309	94.33 310	84.72 311	95.55 273	96.73 233	86.17 307	86.36 324	95.28 242	71.28 328	97.80 286	84.09 301	98.14 121	92.81 355

Effi-MVS+-dtu		93.08 162	93.21 138	92.68 287	96.02 224	83.25 327	97.14 165	96.72 234	93.85 72	91.20 215	93.44 323	83.08 180	98.30 218	91.69 168	95.73 177	96.50 224

TSAR-MVS + GP.		96.69 49	96.49 52	97.27 56	98.31 81	93.39 60	96.79 190	96.72 234	94.17 62	97.44 43	97.66 110	92.76 28	99.33 115	96.86 37	97.76 131	99.08 83

1112_ss		93.37 148	92.42 171	96.21 110	97.05 159	90.99 143	96.31 235	96.72 234	86.87 295	89.83 246	96.69 164	86.51 129	99.14 135	88.12 232	93.67 214	98.50 130

PVSNet	86.66 18	92.24 199	91.74 191	93.73 244	97.77 121	83.69 324	92.88 356	96.72 234	87.91 267	93.00 166	94.86 258	78.51 268	99.05 151	86.53 265	97.45 139	98.47 135

miper_lstm_enhance		90.50 270	90.06 259	91.83 305	95.33 257	83.74 321	93.86 333	96.70 238	87.56 281	87.79 298	93.81 310	83.45 173	96.92 339	87.39 252	84.62 329	94.82 312

v148		90.99 251	90.38 240	92.81 282	93.83 325	85.80 291	96.78 192	96.68 239	89.45 218	88.75 278	93.93 306	82.96 186	97.82 285	87.83 237	83.25 345	94.80 315

ACMH+	87.92 14	90.20 278	89.18 284	93.25 265	96.48 199	86.45 281	96.99 175	96.68 239	88.83 239	84.79 338	96.22 195	70.16 336	98.53 198	84.42 298	88.04 290	94.77 320

CANet_DTU		94.37 109	93.65 118	96.55 78	96.46 200	92.13 98	96.21 243	96.67 241	94.38 58	93.53 154	97.03 147	79.34 251	99.71 46	90.76 183	98.45 109	97.82 178

cl____		90.96 254	90.32 242	92.89 278	95.37 251	86.21 287	94.46 310	96.64 242	87.82 270	88.15 293	94.18 296	82.98 184	97.54 308	87.70 242	85.59 311	94.92 305

HY-MVS	89.66 9	93.87 130	92.95 144	96.63 74	97.10 153	92.49 85	95.64 271	96.64 242	89.05 229	93.00 166	95.79 218	85.77 141	99.45 105	89.16 219	94.35 199	97.96 168

Test_1112_low_res		92.84 176	91.84 187	95.85 129	97.04 160	89.97 179	95.53 275	96.64 242	85.38 317	89.65 252	95.18 245	85.86 139	99.10 139	87.70 242	93.58 219	98.49 132

DIV-MVS_self_test		90.97 253	90.33 241	92.88 279	95.36 252	86.19 288	94.46 310	96.63 245	87.82 270	88.18 292	94.23 293	82.99 183	97.53 310	87.72 239	85.57 312	94.93 303

Fast-Effi-MVS+-dtu		92.29 196	91.99 182	93.21 268	95.27 261	85.52 295	97.03 169	96.63 245	92.09 138	89.11 270	95.14 247	80.33 234	98.08 243	87.54 250	94.74 196	96.03 241

UnsupCasMVSNet_bld		82.13 345	79.46 350	90.14 338	88.00 383	82.47 333	90.89 373	96.62 247	78.94 373	75.61 377	84.40 386	56.63 380	96.31 348	77.30 352	66.77 389	91.63 369

cl22		91.21 241	90.56 236	93.14 270	96.09 222	86.80 272	94.41 312	96.58 248	87.80 272	88.58 281	93.99 304	80.85 225	97.62 302	89.87 197	86.93 300	94.99 298

RRT_MVS		93.10 160	92.83 149	93.93 236	94.76 291	88.04 243	98.47 22	96.55 249	93.44 88	90.01 241	97.04 146	80.64 227	97.93 274	94.33 114	90.21 271	95.83 247

jason		94.84 102	94.39 107	96.18 112	95.52 241	90.93 147	96.09 248	96.52 250	89.28 222	96.01 98	97.32 129	84.70 152	98.77 174	95.15 94	98.91 91	98.85 108
jason: jason.
tt0805		91.09 246	90.07 258	94.16 219	95.61 236	88.31 232	97.56 119	96.51 251	89.56 213	89.17 268	95.64 227	67.08 356	98.38 212	91.07 179	88.44 288	95.80 250

AUN-MVS		91.76 213	90.75 228	94.81 186	97.00 163	88.57 225	96.65 205	96.49 252	89.63 211	92.15 184	96.12 200	78.66 266	98.50 200	90.83 181	79.18 364	97.36 198

hse-mvs2		93.45 146	92.99 142	94.81 186	97.02 161	88.59 224	96.69 201	96.47 253	95.19 20	96.74 64	96.16 199	83.67 168	98.48 203	95.85 69	79.13 365	97.35 200

EG-PatchMatch MVS		87.02 318	85.44 322	91.76 311	92.67 352	85.00 306	96.08 249	96.45 254	83.41 346	79.52 369	93.49 321	57.10 379	97.72 293	79.34 343	90.87 263	92.56 359

KD-MVS_self_test		85.95 329	84.95 328	88.96 348	89.55 377	79.11 369	95.13 293	96.42 255	85.91 310	84.07 347	90.48 359	70.03 338	94.82 368	80.04 335	72.94 379	92.94 353

pmmvs6		87.81 310	86.19 317	92.69 286	91.32 365	86.30 284	97.34 144	96.41 256	80.59 367	84.05 348	94.37 283	67.37 351	97.67 296	84.75 293	79.51 363	94.09 340

PMMVS		92.86 174	92.34 172	94.42 206	94.92 282	86.73 275	94.53 306	96.38 257	84.78 329	94.27 136	95.12 249	83.13 179	98.40 207	91.47 172	96.49 164	98.12 161

RPSCF		90.75 260	90.86 221	90.42 335	96.84 169	76.29 376	95.61 272	96.34 258	83.89 338	91.38 203	97.87 93	76.45 290	98.78 171	87.16 259	92.23 232	96.20 231

MSDG		91.42 229	90.24 248	94.96 177	97.15 150	88.91 217	93.69 339	96.32 259	85.72 313	86.93 319	96.47 183	80.24 235	98.98 157	80.57 332	95.05 190	96.98 210

OurMVSNet-221017-0		90.51 269	90.19 253	91.44 317	93.41 339	81.25 343	96.98 176	96.28 260	91.68 148	86.55 323	96.30 191	74.20 312	97.98 259	88.96 222	87.40 298	95.09 294

MVP-Stereo		90.74 261	90.08 255	92.71 285	93.19 344	88.20 238	95.86 260	96.27 261	86.07 308	84.86 337	94.76 263	77.84 280	97.75 291	83.88 305	98.01 123	92.17 367
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
lupinMVS		94.99 97	94.56 98	96.29 104	96.34 206	91.21 133	95.83 261	96.27 261	88.93 235	96.22 89	96.88 155	86.20 135	98.85 166	95.27 91	99.05 83	98.82 111

BH-untuned		92.94 170	92.62 161	93.92 237	97.22 144	86.16 289	96.40 227	96.25 263	90.06 201	89.79 247	96.17 198	83.19 176	98.35 214	87.19 257	97.27 146	97.24 205

CL-MVSNet_self_test		86.31 324	85.15 326	89.80 342	88.83 380	81.74 341	93.93 330	96.22 264	86.67 297	85.03 335	90.80 358	78.09 276	94.50 369	74.92 363	71.86 381	93.15 351

IS-MVSNet		94.90 99	94.52 102	96.05 118	97.67 125	90.56 161	98.44 23	96.22 264	93.21 96	93.99 143	97.74 104	85.55 143	98.45 204	89.98 193	97.86 126	99.14 76

FA-MVS(test-final)		93.52 144	92.92 145	95.31 158	96.77 177	88.54 227	94.82 298	96.21 266	89.61 212	94.20 138	95.25 243	83.24 175	99.14 135	90.01 192	96.16 168	98.25 151

GA-MVS		91.38 231	90.31 243	94.59 196	94.65 298	87.62 255	94.34 315	96.19 267	90.73 178	90.35 226	93.83 307	71.84 324	97.96 267	87.22 256	93.61 217	98.21 154

IterMVS-SCA-FT		90.31 272	89.81 267	91.82 306	95.52 241	84.20 316	94.30 318	96.15 268	90.61 188	87.39 307	94.27 290	75.80 298	96.44 346	87.34 253	86.88 304	94.82 312

IterMVS		90.15 280	89.67 273	91.61 313	95.48 243	83.72 322	94.33 316	96.12 269	89.99 202	87.31 310	94.15 298	75.78 300	96.27 349	86.97 262	86.89 303	94.83 310
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		92.76 179	91.51 201	96.52 80	98.77 53	90.99 143	97.38 141	96.08 270	82.38 352	89.29 264	97.87 93	83.77 166	99.69 52	81.37 328	96.69 160	98.89 105

pmmvs4		90.93 255	89.85 265	94.17 218	93.34 341	90.79 153	94.60 303	96.02 271	84.62 330	87.45 304	95.15 246	81.88 210	97.45 317	87.70 242	87.87 292	94.27 337

ppachtmachnet_test		88.35 305	87.29 304	91.53 314	92.45 358	83.57 325	93.75 336	95.97 272	84.28 333	85.32 334	94.18 296	79.00 263	96.93 338	75.71 359	84.99 325	94.10 338

Anonymous20240521		86.42 322	85.44 322	89.34 346	90.33 370	79.79 361	96.73 195	95.92 273	83.71 342	83.25 352	91.36 355	63.92 367	96.01 350	78.39 347	85.36 316	92.22 365

ITE_SJBPF				92.43 290	95.34 254	85.37 300		95.92 273	91.47 152	87.75 300	96.39 188	71.00 330	97.96 267	82.36 319	89.86 274	93.97 341

test_fmvs2		89.77 289	89.93 262	89.31 347	93.68 330	76.37 375	97.64 110	95.90 275	89.84 207	91.49 201	96.26 194	58.77 376	97.10 331	94.65 108	91.13 255	94.46 328

USDC		88.94 296	87.83 301	92.27 295	94.66 297	84.96 307	93.86 333	95.90 275	87.34 286	83.40 351	95.56 231	67.43 350	98.19 227	82.64 318	89.67 276	93.66 344

COLMAP_ROB	87.81 15	90.40 271	89.28 282	93.79 242	97.95 110	87.13 267	96.92 180	95.89 277	82.83 349	86.88 321	97.18 138	73.77 316	99.29 121	78.44 346	93.62 216	94.95 299
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VDD-MVS		93.82 133	93.08 140	96.02 121	97.88 116	89.96 180	97.72 98	95.85 278	92.43 127	95.86 102	98.44 44	68.42 346	99.39 111	96.31 49	94.85 191	98.71 118

VDDNet		93.05 164	92.07 178	96.02 121	96.84 169	90.39 168	98.08 51	95.85 278	86.22 306	95.79 105	98.46 42	67.59 349	99.19 128	94.92 99	94.85 191	98.47 135

Vis-MVSNet (Re-imp)		94.15 116	93.88 113	94.95 178	97.61 132	87.92 247	98.10 49	95.80 280	92.22 131	93.02 165	97.45 124	84.53 155	97.91 278	88.24 231	97.97 124	99.02 86

MM		97.29 19	96.98 26	98.23 11	98.01 107	95.03 25	98.07 52	95.76 281	97.78 1	97.52 40	98.80 22	88.09 102	99.86 8	99.44 1	99.37 56	99.80 1

KD-MVS_2432*1600		84.81 336	82.64 340	91.31 319	91.07 367	85.34 301	91.22 368	95.75 282	85.56 315	83.09 353	90.21 362	67.21 352	95.89 352	77.18 353	62.48 392	92.69 356

miper_refine_blended		84.81 336	82.64 340	91.31 319	91.07 367	85.34 301	91.22 368	95.75 282	85.56 315	83.09 353	90.21 362	67.21 352	95.89 352	77.18 353	62.48 392	92.69 356

FE-MVS		92.05 206	91.05 216	95.08 167	96.83 171	87.93 246	93.91 332	95.70 284	86.30 303	94.15 140	94.97 251	76.59 288	99.21 126	84.10 300	96.86 153	98.09 165

tpm cat1		88.36 304	87.21 307	91.81 307	95.13 272	80.55 352	92.58 360	95.70 284	74.97 381	87.45 304	91.96 349	78.01 279	98.17 229	80.39 334	88.74 285	96.72 220

our_test_3		88.78 300	87.98 300	91.20 322	92.45 358	82.53 332	93.61 343	95.69 286	85.77 312	84.88 336	93.71 312	79.99 240	96.78 344	79.47 340	86.24 305	94.28 336

BH-w/o		92.14 204	91.75 189	93.31 263	96.99 164	85.73 292	95.67 267	95.69 286	88.73 245	89.26 266	94.82 261	82.97 185	98.07 247	85.26 288	96.32 167	96.13 237

CR-MVSNet		90.82 258	89.77 269	93.95 232	94.45 306	87.19 264	90.23 376	95.68 288	86.89 294	92.40 175	92.36 342	80.91 222	97.05 333	81.09 331	93.95 212	97.60 190

Patchmtry		88.64 302	87.25 305	92.78 283	94.09 317	86.64 276	89.82 379	95.68 288	80.81 364	87.63 302	92.36 342	80.91 222	97.03 334	78.86 344	85.12 321	94.67 323

iter_conf_final		93.60 139	93.11 139	95.04 169	97.13 151	91.30 128	97.92 73	95.65 290	92.98 112	91.60 197	96.64 168	79.28 253	98.13 232	95.34 90	91.49 246	95.70 260

BH-RMVSNet		92.72 181	91.97 183	94.97 176	97.16 148	87.99 245	96.15 246	95.60 291	90.62 187	91.87 191	97.15 141	78.41 270	98.57 196	83.16 308	97.60 133	98.36 147

PVSNet_0	82.17 19	85.46 333	83.64 336	90.92 325	95.27 261	79.49 365	90.55 374	95.60 291	83.76 341	83.00 355	89.95 364	71.09 329	97.97 262	82.75 316	60.79 394	95.31 282

SCA		91.84 211	91.18 214	93.83 239	95.59 237	84.95 308	94.72 300	95.58 293	90.82 174	92.25 182	93.69 313	75.80 298	98.10 239	86.20 271	95.98 170	98.45 137

AllTest		90.23 276	88.98 287	93.98 228	97.94 111	86.64 276	96.51 218	95.54 294	85.38 317	85.49 331	96.77 158	70.28 334	99.15 133	80.02 336	92.87 221	96.15 235

TestCases				93.98 228	97.94 111	86.64 276		95.54 294	85.38 317	85.49 331	96.77 158	70.28 334	99.15 133	80.02 336	92.87 221	96.15 235

iter_conf05		93.18 158	92.63 159	94.83 183	96.64 183	90.69 157	97.60 115	95.53 296	92.52 125	91.58 198	96.64 168	76.35 293	98.13 232	95.43 88	91.42 249	95.68 262

mvsmamba		93.83 132	93.46 128	94.93 181	94.88 286	90.85 150	98.55 14	95.49 297	94.24 61	91.29 211	96.97 149	83.04 182	98.14 231	95.56 86	91.17 254	95.78 252

tpmvs		89.83 288	89.15 285	91.89 303	94.92 282	80.30 356	93.11 352	95.46 298	86.28 304	88.08 294	92.65 332	80.44 231	98.52 199	81.47 324	89.92 273	96.84 216

pmmvs5		89.86 287	88.87 290	92.82 281	92.86 348	86.23 286	96.26 238	95.39 299	84.24 334	87.12 311	94.51 274	74.27 311	97.36 324	87.61 249	87.57 294	94.86 308

PatchmatchNet		91.91 209	91.35 203	93.59 252	95.38 249	84.11 317	93.15 351	95.39 299	89.54 214	92.10 187	93.68 315	82.82 189	98.13 232	84.81 292	95.32 184	98.52 127
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		91.44 228	91.32 205	91.79 308	95.15 270	79.20 368	93.42 346	95.37 301	88.55 250	93.49 155	93.67 316	82.49 197	98.27 220	90.41 187	89.34 279	97.90 171

Anonymous20231206		87.09 317	86.14 318	89.93 341	91.22 366	80.35 354	96.11 247	95.35 302	83.57 344	84.16 343	93.02 328	73.54 318	95.61 360	72.16 374	86.14 307	93.84 343

MIMVSNet1		84.93 335	83.05 337	90.56 333	89.56 376	84.84 310	95.40 280	95.35 302	83.91 337	80.38 365	92.21 346	57.23 378	93.34 381	70.69 380	82.75 351	93.50 346

TDRefinement		86.53 320	84.76 331	91.85 304	82.23 393	84.25 314	96.38 229	95.35 302	84.97 326	84.09 346	94.94 253	65.76 363	98.34 217	84.60 296	74.52 375	92.97 352

TR-MVS		91.48 227	90.59 234	94.16 219	96.40 203	87.33 257	95.67 267	95.34 305	87.68 278	91.46 202	95.52 234	76.77 287	98.35 214	82.85 313	93.61 217	96.79 218

EPNet_dtu		91.71 214	91.28 208	92.99 274	93.76 327	83.71 323	96.69 201	95.28 306	93.15 102	87.02 315	95.95 207	83.37 174	97.38 323	79.46 341	96.84 154	97.88 173
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet5		87.29 314	85.79 320	91.78 309	94.80 290	87.28 259	95.49 277	95.28 306	84.09 336	83.85 350	91.82 350	62.95 370	94.17 373	78.48 345	85.34 317	93.91 342

MDTV_nov1_ep13			90.76 227		95.22 265	80.33 355	93.03 354	95.28 306	88.14 262	92.84 172	93.83 307	81.34 216	98.08 243	82.86 311	94.34 200

LF4IMVS		87.94 308	87.25 305	89.98 340	92.38 360	80.05 360	94.38 313	95.25 309	87.59 280	84.34 340	94.74 265	64.31 366	97.66 298	84.83 291	87.45 295	92.23 364

TransMVSNet (Re)		88.94 296	87.56 302	93.08 272	94.35 309	88.45 231	97.73 95	95.23 310	87.47 282	84.26 342	95.29 240	79.86 243	97.33 325	79.44 342	74.44 376	93.45 348

test20.03		86.14 327	85.40 324	88.35 349	90.12 371	80.06 359	95.90 259	95.20 311	88.59 246	81.29 360	93.62 318	71.43 327	92.65 383	71.26 378	81.17 356	92.34 363

new-patchmatchnet		83.18 342	81.87 345	87.11 356	86.88 385	75.99 377	93.70 337	95.18 312	85.02 325	77.30 376	88.40 374	65.99 361	93.88 378	74.19 368	70.18 383	91.47 373

MDA-MVSNet_test_wron		85.87 330	84.23 334	90.80 330	92.38 360	82.57 331	93.17 349	95.15 313	82.15 353	67.65 385	92.33 345	78.20 272	95.51 363	77.33 350	79.74 360	94.31 335

YYNet1		85.87 330	84.23 334	90.78 331	92.38 360	82.46 334	93.17 349	95.14 314	82.12 354	67.69 384	92.36 342	78.16 275	95.50 364	77.31 351	79.73 361	94.39 331

Baseline_NR-MVSNet		91.20 242	90.62 232	92.95 276	93.83 325	88.03 244	97.01 174	95.12 315	88.42 254	89.70 249	95.13 248	83.47 171	97.44 318	89.66 203	83.24 346	93.37 349

thres200		92.23 200	91.39 202	94.75 193	97.61 132	89.03 215	96.60 213	95.09 316	92.08 139	93.28 161	94.00 303	78.39 271	99.04 154	81.26 330	94.18 203	96.19 232

ADS-MVSNet		89.89 284	88.68 292	93.53 255	95.86 226	84.89 309	90.93 371	95.07 317	83.23 347	91.28 212	91.81 351	79.01 261	97.85 281	79.52 338	91.39 250	97.84 175

pmmvs-eth3d		86.22 325	84.45 332	91.53 314	88.34 382	87.25 261	94.47 308	95.01 318	83.47 345	79.51 370	89.61 367	69.75 340	95.71 357	83.13 309	76.73 372	91.64 368

Anonymous202405211		92.07 205	90.83 225	95.76 131	98.19 95	88.75 220	97.58 117	95.00 319	86.00 309	93.64 150	97.45 124	66.24 360	99.53 91	90.68 186	92.71 226	99.01 89

MDA-MVSNet-bldmvs		85.00 334	82.95 339	91.17 323	93.13 346	83.33 326	94.56 305	95.00 319	84.57 331	65.13 389	92.65 332	70.45 333	95.85 354	73.57 370	77.49 368	94.33 333

ambc				86.56 359	83.60 390	70.00 386	85.69 388	94.97 321		80.60 364	88.45 373	37.42 392	96.84 342	82.69 317	75.44 374	92.86 354

testgi		87.97 307	87.21 307	90.24 337	92.86 348	80.76 347	96.67 204	94.97 321	91.74 146	85.52 330	95.83 213	62.66 371	94.47 371	76.25 357	88.36 289	95.48 266

dp		88.90 298	88.26 298	90.81 328	94.58 302	76.62 374	92.85 357	94.93 323	85.12 323	90.07 240	93.07 327	75.81 297	98.12 237	80.53 333	87.42 297	97.71 182

test_fmvs3		83.21 341	83.02 338	83.78 363	86.77 386	68.34 389	96.76 193	94.91 324	86.49 300	84.14 345	89.48 368	36.04 393	91.73 385	91.86 162	80.77 358	91.26 375

test_0402		86.46 321	84.79 330	91.45 316	95.02 276	85.55 294	96.29 237	94.89 325	80.90 361	82.21 357	93.97 305	68.21 347	97.29 327	62.98 386	88.68 286	91.51 371

tfpn200view9		92.38 189	91.52 199	94.95 178	97.85 117	89.29 205	97.41 134	94.88 326	92.19 135	93.27 162	94.46 279	78.17 273	99.08 144	81.40 325	94.08 207	96.48 225

CVMVSNet		91.23 240	91.75 189	89.67 343	95.77 231	74.69 378	96.44 219	94.88 326	85.81 311	92.18 183	97.64 114	79.07 256	95.58 362	88.06 233	95.86 174	98.74 115

thres400		92.42 187	91.52 199	95.12 166	97.85 117	89.29 205	97.41 134	94.88 326	92.19 135	93.27 162	94.46 279	78.17 273	99.08 144	81.40 325	94.08 207	96.98 210

EPNet		95.20 90	94.56 98	97.14 63	92.80 350	92.68 79	97.85 82	94.87 329	96.64 3	92.46 174	97.80 101	86.23 132	99.65 58	93.72 127	98.62 100	99.10 82
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SixPastTwentyTwo		89.15 294	88.54 294	90.98 324	93.49 336	80.28 357	96.70 199	94.70 330	90.78 175	84.15 344	95.57 230	71.78 325	97.71 294	84.63 295	85.07 322	94.94 301

thres100view900		92.43 186	91.58 196	94.98 175	97.92 113	89.37 201	97.71 100	94.66 331	92.20 133	93.31 160	94.90 256	78.06 277	99.08 144	81.40 325	94.08 207	96.48 225

thres600view7		92.49 185	91.60 195	95.18 162	97.91 114	89.47 195	97.65 106	94.66 331	92.18 137	93.33 159	94.91 255	78.06 277	99.10 139	81.61 322	94.06 211	96.98 210

PatchT		88.87 299	87.42 303	93.22 267	94.08 318	85.10 305	89.51 380	94.64 333	81.92 355	92.36 178	88.15 377	80.05 239	97.01 336	72.43 373	93.65 215	97.54 193

baseline1		92.82 177	91.90 185	95.55 147	97.20 146	90.77 154	97.19 160	94.58 334	92.20 133	92.36 178	96.34 190	84.16 162	98.21 224	89.20 217	83.90 341	97.68 184

Gipuma		67.86 360	65.41 362	75.18 376	92.66 353	73.45 381	66.50 395	94.52 335	53.33 394	57.80 395	66.07 395	30.81 395	89.20 389	48.15 395	78.88 367	62.90 395
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CostFormer		91.18 245	90.70 230	92.62 288	94.84 288	81.76 340	94.09 325	94.43 336	84.15 335	92.72 173	93.77 311	79.43 250	98.20 225	90.70 185	92.18 235	97.90 171

tpm2		89.96 282	89.21 283	92.23 297	94.91 284	81.25 343	93.78 335	94.42 337	80.62 366	91.56 199	93.44 323	76.44 291	97.94 271	85.60 283	92.08 239	97.49 194

JIA-IIPM		88.26 306	87.04 310	91.91 302	93.52 334	81.42 342	89.38 381	94.38 338	80.84 363	90.93 217	80.74 388	79.22 254	97.92 275	82.76 315	91.62 243	96.38 228

dmvs_re		90.21 277	89.50 278	92.35 291	95.47 246	85.15 303	95.70 266	94.37 339	90.94 173	88.42 283	93.57 319	74.63 308	95.67 359	82.80 314	89.57 277	96.22 230

Patchmatch-test		89.42 292	87.99 299	93.70 247	95.27 261	85.11 304	88.98 382	94.37 339	81.11 360	87.10 313	93.69 313	82.28 201	97.50 313	74.37 366	94.76 194	98.48 134

LCM-MVSNet		72.55 354	69.39 358	82.03 365	70.81 403	65.42 394	90.12 378	94.36 341	55.02 393	65.88 387	81.72 387	24.16 401	89.96 386	74.32 367	68.10 387	90.71 378

ADS-MVSNet2		89.45 291	88.59 293	92.03 300	95.86 226	82.26 336	90.93 371	94.32 342	83.23 347	91.28 212	91.81 351	79.01 261	95.99 351	79.52 338	91.39 250	97.84 175

EU-MVSNet		88.72 301	88.90 289	88.20 351	93.15 345	74.21 379	96.63 210	94.22 343	85.18 321	87.32 309	95.97 205	76.16 294	94.98 367	85.27 287	86.17 306	95.41 272

MVS_0304		97.04 28	96.73 42	97.96 23	97.60 134	94.36 34	98.01 57	94.09 344	97.33 2	96.29 86	98.79 24	89.73 82	99.86 8	99.36 2	99.42 46	99.67 13

MIMVSNet		88.50 303	86.76 313	93.72 246	94.84 288	87.77 253	91.39 366	94.05 345	86.41 302	87.99 296	92.59 335	63.27 368	95.82 356	77.44 349	92.84 223	97.57 192

OpenMVS_ROB	81.14 20	84.42 338	82.28 344	90.83 326	90.06 372	84.05 319	95.73 265	94.04 346	73.89 383	80.17 368	91.53 354	59.15 375	97.64 299	66.92 384	89.05 281	90.80 377

TinyColmap		86.82 319	85.35 325	91.21 321	94.91 284	82.99 329	93.94 329	94.02 347	83.58 343	81.56 359	94.68 267	62.34 372	98.13 232	75.78 358	87.35 299	92.52 361

ETVMVS		90.52 268	89.14 286	94.67 195	96.81 174	87.85 251	95.91 258	93.97 348	89.71 210	92.34 180	92.48 337	65.41 364	97.96 267	81.37 328	94.27 201	98.21 154

IB-MVS	87.33 17	89.91 283	88.28 297	94.79 190	95.26 264	87.70 254	95.12 294	93.95 349	89.35 221	87.03 314	92.49 336	70.74 332	99.19 128	89.18 218	81.37 355	97.49 194
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
Syy-MVS		87.13 316	87.02 311	87.47 354	95.16 268	73.21 382	95.00 295	93.93 350	88.55 250	86.96 316	91.99 347	75.90 295	94.00 375	61.59 388	94.11 204	95.20 290

myMVS_eth3d		87.18 315	86.38 315	89.58 344	95.16 268	79.53 363	95.00 295	93.93 350	88.55 250	86.96 316	91.99 347	56.23 381	94.00 375	75.47 362	94.11 204	95.20 290

testing222		90.31 272	88.96 288	94.35 209	96.54 194	87.29 258	95.50 276	93.84 352	90.97 172	91.75 195	92.96 329	62.18 373	98.00 257	82.86 311	94.08 207	97.76 180

test_f		80.57 347	79.62 349	83.41 364	83.38 391	67.80 391	93.57 344	93.72 353	80.80 365	77.91 375	87.63 380	33.40 394	92.08 384	87.14 260	79.04 366	90.34 379

LCM-MVSNet-Re		92.50 183	92.52 167	92.44 289	96.82 173	81.89 339	96.92 180	93.71 354	92.41 128	84.30 341	94.60 271	85.08 148	97.03 334	91.51 170	97.36 141	98.40 143

bld_raw_dy_0_64		92.37 190	91.69 192	94.39 207	94.28 314	89.73 185	97.71 100	93.65 355	92.78 120	90.46 223	96.67 166	75.88 296	97.97 262	92.92 146	90.89 262	95.48 266

tpm		90.25 275	89.74 272	91.76 311	93.92 321	79.73 362	93.98 326	93.54 356	88.28 257	91.99 189	93.25 326	77.51 283	97.44 318	87.30 255	87.94 291	98.12 161

ET-MVSNet_ETH3D		91.49 226	90.11 254	95.63 141	96.40 203	91.57 118	95.34 282	93.48 357	90.60 190	75.58 378	95.49 235	80.08 238	96.79 343	94.25 115	89.76 275	98.52 127

LFMVS		93.60 139	92.63 159	96.52 80	98.13 100	91.27 130	97.94 71	93.39 358	90.57 191	96.29 86	98.31 60	69.00 342	99.16 132	94.18 116	95.87 173	99.12 80

Patchmatch-RL test		87.38 313	86.24 316	90.81 328	88.74 381	78.40 372	88.12 386	93.17 359	87.11 291	82.17 358	89.29 369	81.95 208	95.60 361	88.64 228	77.02 369	98.41 142

test-LLR		91.42 229	91.19 213	92.12 298	94.59 300	80.66 349	94.29 319	92.98 360	91.11 168	90.76 219	92.37 339	79.02 259	98.07 247	88.81 224	96.74 157	97.63 185

test-mter		90.19 279	89.54 277	92.12 298	94.59 300	80.66 349	94.29 319	92.98 360	87.68 278	90.76 219	92.37 339	67.67 348	98.07 247	88.81 224	96.74 157	97.63 185

WB-MVSnew		89.88 285	89.56 276	90.82 327	94.57 303	83.06 328	95.65 270	92.85 362	87.86 269	90.83 218	94.10 299	79.66 247	96.88 340	76.34 356	94.19 202	92.54 360

testing3		87.67 311	86.88 312	90.05 339	96.14 218	80.71 348	97.10 167	92.85 362	90.15 199	87.54 303	94.55 273	55.70 382	94.10 374	73.77 369	94.10 206	95.35 279

test_method		66.11 361	64.89 363	69.79 378	72.62 401	35.23 409	65.19 396	92.83 364	20.35 399	65.20 388	88.08 378	43.14 390	82.70 396	73.12 372	63.46 391	91.45 374

test0.0.03 1		89.37 293	88.70 291	91.41 318	92.47 357	85.63 293	95.22 291	92.70 365	91.11 168	86.91 320	93.65 317	79.02 259	93.19 382	78.00 348	89.18 280	95.41 272

new_pmnet		82.89 343	81.12 348	88.18 352	89.63 375	80.18 358	91.77 365	92.57 366	76.79 380	75.56 379	88.23 376	61.22 374	94.48 370	71.43 376	82.92 349	89.87 380

mvsany_test1		93.93 128	93.98 111	93.78 243	94.94 281	86.80 272	94.62 302	92.55 367	88.77 244	96.85 60	98.49 38	88.98 88	98.08 243	95.03 96	95.62 180	96.46 227

thisisatest0515		92.29 196	91.30 207	95.25 160	96.60 186	88.90 218	94.36 314	92.32 368	87.92 266	93.43 157	94.57 272	77.28 284	99.00 155	89.42 208	95.86 174	97.86 174

thisisatest0530		93.03 165	92.21 176	95.49 151	97.07 154	89.11 214	97.49 130	92.19 369	90.16 198	94.09 141	96.41 186	76.43 292	99.05 151	90.38 188	95.68 179	98.31 149

tttt0517		92.96 168	92.33 173	94.87 182	97.11 152	87.16 266	97.97 67	92.09 370	90.63 186	93.88 147	97.01 148	76.50 289	99.06 150	90.29 191	95.45 182	98.38 145

K. test v3		87.64 312	86.75 314	90.32 336	93.02 347	79.48 366	96.61 211	92.08 371	90.66 184	80.25 367	94.09 300	67.21 352	96.65 345	85.96 279	80.83 357	94.83 310

TESTMET0.1,1		90.06 281	89.42 279	91.97 301	94.41 308	80.62 351	94.29 319	91.97 372	87.28 288	90.44 224	92.47 338	68.79 343	97.67 296	88.50 230	96.60 162	97.61 189

PM-MVS		83.48 340	81.86 346	88.31 350	87.83 384	77.59 373	93.43 345	91.75 373	86.91 293	80.63 363	89.91 365	44.42 389	95.84 355	85.17 290	76.73 372	91.50 372

baseline2		91.63 217	90.86 221	93.94 234	94.33 310	86.32 283	95.92 257	91.64 374	89.37 220	86.94 318	94.69 266	81.62 214	98.69 183	88.64 228	94.57 198	96.81 217

APD_test1		79.31 349	77.70 352	84.14 362	89.11 379	69.07 388	92.36 364	91.50 375	69.07 386	73.87 380	92.63 334	39.93 391	94.32 372	70.54 381	80.25 359	89.02 382

FPMVS		71.27 355	69.85 357	75.50 375	74.64 398	59.03 398	91.30 367	91.50 375	58.80 390	57.92 394	88.28 375	29.98 397	85.53 395	53.43 393	82.84 350	81.95 388

door								91.13 377

door-mid								91.06 378

EGC-MVSNET		68.77 359	63.01 364	86.07 361	92.49 356	82.24 337	93.96 328	90.96 379	0.71 404	2.62 405	90.89 357	53.66 383	93.46 379	57.25 391	84.55 331	82.51 387

mvsany_test3		83.59 339	82.44 343	87.03 357	83.80 389	73.82 380	93.70 337	90.92 380	86.42 301	82.51 356	90.26 361	46.76 388	95.71 357	90.82 182	76.76 371	91.57 370

pmmvs3		79.97 348	77.50 353	87.39 355	82.80 392	79.38 367	92.70 359	90.75 381	70.69 385	78.66 372	87.47 382	51.34 386	93.40 380	73.39 371	69.65 384	89.38 381

DSMNet-mixed		86.34 323	86.12 319	87.00 358	89.88 374	70.43 384	94.93 297	90.08 382	77.97 377	85.42 333	92.78 331	74.44 310	93.96 377	74.43 365	95.14 186	96.62 221

MVS-HIRNet		82.47 344	81.21 347	86.26 360	95.38 249	69.21 387	88.96 383	89.49 383	66.28 387	80.79 362	74.08 392	68.48 345	97.39 322	71.93 375	95.47 181	92.18 366

WB-MVS		76.77 351	76.63 354	77.18 370	85.32 387	56.82 400	94.53 306	89.39 384	82.66 351	71.35 382	89.18 370	75.03 305	88.88 390	35.42 398	66.79 388	85.84 384

test1111		93.19 155	92.82 150	94.30 214	97.58 137	84.56 312	98.21 43	89.02 385	93.53 84	94.58 130	98.21 67	72.69 320	99.05 151	93.06 140	98.48 107	99.28 65

SSC-MVS		76.05 352	75.83 355	76.72 374	84.77 388	56.22 401	94.32 317	88.96 386	81.82 357	70.52 383	88.91 371	74.79 307	88.71 391	33.69 399	64.71 390	85.23 385

ECVR-MVS		93.19 155	92.73 156	94.57 201	97.66 127	85.41 297	98.21 43	88.23 387	93.43 89	94.70 128	98.21 67	72.57 321	99.07 148	93.05 141	98.49 105	99.25 68

EPMVS		90.70 263	89.81 267	93.37 261	94.73 295	84.21 315	93.67 340	88.02 388	89.50 216	92.38 177	93.49 321	77.82 281	97.78 288	86.03 277	92.68 227	98.11 164

ANet_high		63.94 362	59.58 365	77.02 371	61.24 405	66.06 392	85.66 389	87.93 389	78.53 375	42.94 397	71.04 394	25.42 400	80.71 397	52.60 394	30.83 398	84.28 386

PMMVS2		70.19 356	66.92 359	80.01 366	76.35 397	65.67 393	86.22 387	87.58 390	64.83 389	62.38 390	80.29 389	26.78 399	88.49 393	63.79 385	54.07 395	85.88 383

lessismore_v0				90.45 334	91.96 363	79.09 370		87.19 391		80.32 366	94.39 281	66.31 359	97.55 307	84.00 303	76.84 370	94.70 322

PMVS	53.92 22	58.58 363	55.40 366	68.12 379	51.00 406	48.64 403	78.86 392	87.10 392	46.77 395	35.84 401	74.28 391	8.76 405	86.34 394	42.07 396	73.91 377	69.38 393
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_vis1_rt		86.16 326	85.06 327	89.46 345	93.47 338	80.46 353	96.41 223	86.61 393	85.22 320	79.15 371	88.64 372	52.41 385	97.06 332	93.08 139	90.57 265	90.87 376

testf1		69.31 357	66.76 360	76.94 372	78.61 395	61.93 396	88.27 384	86.11 394	55.62 391	59.69 391	85.31 384	20.19 403	89.32 387	57.62 389	69.44 385	79.58 389

APD_test2		69.31 357	66.76 360	76.94 372	78.61 395	61.93 396	88.27 384	86.11 394	55.62 391	59.69 391	85.31 384	20.19 403	89.32 387	57.62 389	69.44 385	79.58 389

gg-mvs-nofinetune		87.82 309	85.61 321	94.44 204	94.46 305	89.27 208	91.21 370	84.61 396	80.88 362	89.89 245	74.98 390	71.50 326	97.53 310	85.75 282	97.21 148	96.51 223

dmvs_testset		81.38 346	82.60 342	77.73 369	91.74 364	51.49 402	93.03 354	84.21 397	89.07 227	78.28 374	91.25 356	76.97 286	88.53 392	56.57 392	82.24 352	93.16 350

GG-mvs-BLEND				93.62 250	93.69 329	89.20 210	92.39 363	83.33 398		87.98 297	89.84 366	71.00 330	96.87 341	82.08 321	95.40 183	94.80 315

MTMP							97.86 79	82.03 399

DeepMVS_CX				74.68 377	90.84 369	64.34 395		81.61 400	65.34 388	67.47 386	88.01 379	48.60 387	80.13 398	62.33 387	73.68 378	79.58 389

E-PMN		53.28 364	52.56 368	55.43 381	74.43 399	47.13 404	83.63 391	76.30 401	42.23 396	42.59 398	62.22 397	28.57 398	74.40 399	31.53 400	31.51 397	44.78 396

test2506		91.60 218	90.78 226	94.04 225	97.66 127	83.81 320	98.27 33	75.53 402	93.43 89	95.23 119	98.21 67	67.21 352	99.07 148	93.01 144	98.49 105	99.25 68

EMVS		52.08 366	51.31 369	54.39 382	72.62 401	45.39 406	83.84 390	75.51 403	41.13 397	40.77 399	59.65 398	30.08 396	73.60 400	28.31 401	29.90 399	44.18 397

test_vis3_rt		72.73 353	70.55 356	79.27 367	80.02 394	68.13 390	93.92 331	74.30 404	76.90 379	58.99 393	73.58 393	20.29 402	95.37 365	84.16 299	72.80 380	74.31 392

MVE	50.73 23	53.25 365	48.81 370	66.58 380	65.34 404	57.50 399	72.49 394	70.94 405	40.15 398	39.28 400	63.51 396	6.89 407	73.48 401	38.29 397	42.38 396	68.76 394
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		51.94 367	53.82 367	46.29 383	33.73 407	45.30 407	78.32 393	67.24 406	18.02 400	50.93 396	87.05 383	52.99 384	53.11 402	70.76 379	25.29 400	40.46 398

N_pmnet		78.73 350	78.71 351	78.79 368	92.80 350	46.50 405	94.14 323	43.71 407	78.61 374	80.83 361	91.66 353	74.94 306	96.36 347	67.24 383	84.45 333	93.50 346

wuyk23d		25.11 368	24.57 372	26.74 384	73.98 400	39.89 408	57.88 397	9.80 408	12.27 401	10.39 402	6.97 404	7.03 406	36.44 403	25.43 402	17.39 401	3.89 401

testmvs		13.36 370	16.33 373	4.48 386	5.04 408	2.26 411	93.18 348	3.28 409	2.70 402	8.24 403	21.66 400	2.29 409	2.19 404	7.58 403	2.96 402	9.00 400

test123		13.04 371	15.66 374	5.18 385	4.51 409	3.45 410	92.50 362	1.81 410	2.50 403	7.58 404	20.15 401	3.67 408	2.18 405	7.13 404	1.07 403	9.90 399

test_blank		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

uanet_test		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

DCPMVS		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

pcd_1.5k_mvsjas		7.39 373	9.85 376	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	88.65 95	0.00 406	0.00 405	0.00 404	0.00 402

sosnet-low-res		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

sosnet		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

uncertanet		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

Regformer		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

n2								0.00 411

nn								0.00 411

ab-mvs-re		8.06 372	10.74 375	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	96.69 164	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

uanet		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

WAC-MVS						79.53 363								75.56 361

PC_three_1452									90.77 176	98.89 14	98.28 65	96.24 1	98.35 214	95.76 73	99.58 21	99.59 22

eth-test2					0.00 410

eth-test					0.00 410

OPU-MVS				98.55 3	98.82 52	96.86 3	98.25 36				98.26 66	96.04 2	99.24 124	95.36 89	99.59 17	99.56 29

test_0728_THIRD									94.78 41	98.73 18	98.87 15	95.87 4	99.84 23	97.45 26	99.72 2	99.77 2

GSMVS																98.45 137

test_part2					99.28 25	95.74 8				98.10 29

sam_mvs1												82.76 190				98.45 137

sam_mvs												81.94 209

test_post1							92.81 358				16.58 403	80.53 229	97.68 295	86.20 271

test_post											17.58 402	81.76 211	98.08 243

patchmatchnet-post											90.45 360	82.65 194	98.10 239

gm-plane-assit					93.22 343	78.89 371			84.82 328		93.52 320		98.64 188	87.72 239

test9_res														94.81 103	99.38 53	99.45 47

agg_prior2														93.94 121	99.38 53	99.50 40

test_prior4						93.66 55	96.42 222

test_prior2							96.35 231		92.80 119	96.03 95	97.59 118	92.01 43		95.01 97	99.38 53

旧先验2							95.94 256		81.66 358	97.34 48			98.82 168	92.26 149

新几何2							95.79 263

原ACMM2							95.67 267

testdata2													99.67 56	85.96 279

segment_acmp												92.89 27

testdata1							95.26 290		93.10 105

plane_prior7					96.21 210	89.98 178

plane_prior6					96.10 221	90.00 174						81.32 217

plane_prior4											96.64 168

plane_prior3						90.00 174			94.46 54	91.34 205

plane_prior2							97.74 93		94.85 34

plane_prior1					96.14 218

plane_prior						89.99 176	97.24 153		94.06 65						92.16 236

HQP5-MVS						89.33 203

HQP-NCC					95.86 226		96.65 205		93.55 80	90.14 229

ACMP_Plane					95.86 226		96.65 205		93.55 80	90.14 229

BP-MVS														92.13 155

HQP4-MVS										90.14 229			98.50 200			95.78 252

HQP2-MVS												80.95 220

NP-MVS					95.99 225	89.81 183					95.87 210

MDTV_nov1_ep13_2view						70.35 385	93.10 353		83.88 339	93.55 152		82.47 198		86.25 270		98.38 145

ACMMP++_ref															90.30 270

ACMMP++															91.02 258

Test By Simon												88.73 94

High-resolution multi-view benchmark