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 88	98.41 77	91.73 116	98.01 60	99.02 1	96.37 7	99.30 2	98.92 16	92.39 41	99.79 36	99.16 7	99.46 41	98.08 180

PGM-MVS		96.81 46	96.53 55	97.65 42	99.35 20	93.53 60	97.65 112	98.98 2	92.22 140	97.14 61	98.44 49	91.17 67	99.85 18	94.35 127	99.46 41	99.57 28

MVS_111021_HR		96.68 56	96.58 54	96.99 75	98.46 73	92.31 98	96.20 257	98.90 3	94.30 71	95.86 114	97.74 112	92.33 42	99.38 119	96.04 77	99.42 50	99.28 68

test_fmvsmconf_n		97.49 15	97.56 9	97.29 58	97.44 149	92.37 95	97.91 76	98.88 4	95.83 11	98.92 15	99.05 8	91.45 57	99.80 33	99.12 8	99.46 41	99.69 12

ACMMP		96.27 71	95.93 74	97.28 60	99.24 28	92.62 87	98.25 35	98.81 5	92.99 118	94.56 145	98.39 53	88.96 94	99.85 18	94.57 125	97.63 144	99.36 63
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 72	96.19 71	96.39 105	98.23 94	91.35 136	96.24 255	98.79 6	93.99 77	95.80 116	97.65 119	89.92 86	99.24 131	95.87 81	99.20 76	98.58 136

patch_mono-2		96.83 45	97.44 16	95.01 184	99.05 39	85.39 311	96.98 188	98.77 7	94.70 51	97.99 36	98.66 32	93.61 19	99.91 1	97.67 27	99.50 35	99.72 11

fmvsm_s_conf0.5_n		96.85 42	97.13 20	96.04 129	98.07 108	90.28 178	97.97 69	98.76 8	94.93 36	98.84 19	99.06 7	88.80 97	99.65 64	99.06 9	98.63 107	98.18 169

fmvsm_l_conf0.5_n		97.65 7	97.75 6	97.34 55	98.21 95	92.75 83	97.83 88	98.73 9	95.04 34	99.30 2	98.84 26	93.34 22	99.78 39	99.32 3	99.13 84	99.50 43

fmvsm_s_conf0.5_n_a		96.75 50	96.93 33	96.20 121	97.64 136	90.72 164	98.00 61	98.73 9	94.55 58	98.91 16	99.08 4	88.22 106	99.63 73	98.91 12	98.37 120	98.25 164

FC-MVSNet-test		93.94 142	93.57 135	95.04 182	95.48 264	91.45 133	98.12 50	98.71 11	93.37 101	90.23 246	96.70 173	87.66 116	97.85 298	91.49 184	90.39 285	95.83 268

UniMVSNet (Re)		93.31 163	92.55 175	95.61 156	95.39 269	93.34 66	97.39 149	98.71 11	93.14 114	90.10 255	94.83 272	87.71 115	98.03 272	91.67 182	83.99 355	95.46 287

fmvsm_l_conf0.5_n_a		97.63 8	97.76 5	97.26 62	98.25 89	92.59 89	97.81 92	98.68 13	94.93 36	99.24 5	98.87 21	93.52 20	99.79 36	99.32 3	99.21 74	99.40 57

FIs		94.09 136	93.70 131	95.27 172	95.70 254	92.03 109	98.10 51	98.68 13	93.36 103	90.39 243	96.70 173	87.63 119	97.94 289	92.25 164	90.50 284	95.84 267

WR-MVS_H		92.00 217	91.35 214	93.95 245	95.09 295	89.47 204	98.04 58	98.68 13	91.46 164	88.34 304	94.68 279	85.86 148	97.56 325	85.77 299	84.24 353	94.82 330

VPA-MVSNet		93.24 165	92.48 180	95.51 162	95.70 254	92.39 94	97.86 81	98.66 16	92.30 139	92.09 205	95.37 248	80.49 243	98.40 227	93.95 133	85.86 326	95.75 276

fmvsm_s_conf0.5_n_3		97.15 26	97.36 18	96.52 89	97.98 114	91.19 144	97.84 85	98.65 17	97.08 2	99.25 4	99.10 3	87.88 113	99.79 36	99.32 3	99.18 78	98.59 135

fmvsm_s_conf0.5_n_2		96.62 57	96.82 42	96.02 131	97.98 114	90.43 174	97.50 133	98.59 18	96.59 4	99.31 1	99.08 4	84.47 165	99.75 45	99.37 2	98.45 117	97.88 190

UniMVSNet_NR-MVSNet		93.37 161	92.67 170	95.47 167	95.34 275	92.83 81	97.17 172	98.58 19	92.98 123	90.13 251	95.80 225	88.37 105	97.85 298	91.71 179	83.93 356	95.73 278

CSCG		96.05 75	95.91 75	96.46 99	99.24 28	90.47 171	98.30 28	98.57 20	89.01 249	93.97 161	97.57 127	92.62 37	99.76 42	94.66 120	99.27 67	99.15 78

MSLP-MVS++		96.94 36	97.06 23	96.59 85	98.72 58	91.86 114	97.67 109	98.49 21	94.66 54	97.24 57	98.41 52	92.31 44	98.94 174	96.61 54	99.46 41	98.96 98

HyFIR lowres test		93.66 152	92.92 158	95.87 139	98.24 90	89.88 191	94.58 325	98.49 21	85.06 344	93.78 164	95.78 229	82.86 199	98.67 205	91.77 177	95.71 191	99.07 89

CHOSEN 1792x2688		94.15 131	93.51 141	96.06 127	98.27 86	89.38 209	95.18 311	98.48 23	85.60 334	93.76 165	97.11 153	83.15 190	99.61 75	91.33 187	98.72 104	99.19 74

PHI-MVS		96.77 48	96.46 62	97.71 40	98.40 78	94.07 48	98.21 42	98.45 24	89.86 221	97.11 63	98.01 89	92.52 39	99.69 58	96.03 78	99.53 29	99.36 63

fmvsm_s_conf0.1_n		96.58 60	96.77 46	96.01 134	96.67 195	90.25 179	97.91 76	98.38 25	94.48 62	98.84 19	99.14 1	88.06 108	99.62 74	98.82 14	98.60 109	98.15 173

PVSNet_BlendedMVS		94.06 137	93.92 127	94.47 215	98.27 86	89.46 206	96.73 208	98.36 26	90.17 213	94.36 150	95.24 256	88.02 109	99.58 83	93.44 144	90.72 280	94.36 350

PVSNet_Blended		94.87 113	94.56 111	95.81 143	98.27 86	89.46 206	95.47 295	98.36 26	88.84 257	94.36 150	96.09 214	88.02 109	99.58 83	93.44 144	98.18 128	98.40 156

3Dnovator	91.36 5	95.19 103	94.44 119	97.44 52	96.56 204	93.36 65	98.65 11	98.36 26	94.12 73	89.25 284	98.06 83	82.20 215	99.77 41	93.41 146	99.32 64	99.18 75

FOURS1					99.55 1	93.34 66	99.29 1	98.35 29	94.98 35	98.49 26

DPE-MVS		97.86 4	97.65 8	98.47 5	99.17 32	95.78 7	97.21 169	98.35 29	95.16 28	98.71 23	98.80 28	95.05 10	99.89 3	96.70 52	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 65	96.47 59	96.16 123	95.48 264	90.69 165	97.91 76	98.33 31	94.07 74	98.93 12	99.14 1	87.44 126	99.61 75	98.63 16	98.32 122	98.18 169

HFP-MVS		97.14 27	96.92 34	97.83 26	99.42 7	94.12 46	98.52 15	98.32 32	93.21 106	97.18 58	98.29 69	92.08 46	99.83 26	95.63 94	99.59 19	99.54 36

ACMMPR		97.07 30	96.84 38	97.79 30	99.44 6	93.88 52	98.52 15	98.31 33	93.21 106	97.15 60	98.33 63	91.35 61	99.86 9	95.63 94	99.59 19	99.62 20

test_fmvsmvis_n_1920		96.70 52	96.84 38	96.31 110	96.62 197	91.73 116	97.98 63	98.30 34	96.19 8	96.10 105	98.95 14	89.42 88	99.76 42	98.90 13	99.08 88	97.43 216

APDe-MVS		97.82 5	97.73 7	98.08 18	99.15 33	94.82 28	98.81 7	98.30 34	94.76 49	98.30 29	98.90 18	93.77 17	99.68 60	97.93 19	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 27	98.29 36	94.92 38	98.99 10	98.92 16	95.08 8

MSP-MVS		97.59 10	97.54 10	97.73 37	99.40 11	93.77 56	98.53 14	98.29 36	95.55 19	98.56 25	97.81 107	93.90 15	99.65 64	96.62 53	99.21 74	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 61	95.39 11	99.29 1	98.28 38	94.78 47	98.93 12	98.87 21	96.04 2	99.86 9	97.45 35	99.58 23	99.59 24

test_0728_SECOND				98.51 4	99.45 3	95.93 5	98.21 42	98.28 38					99.86 9	97.52 31	99.67 6	99.75 6

CP-MVS		97.02 32	96.81 43	97.64 44	99.33 21	93.54 59	98.80 8	98.28 38	92.99 118	96.45 92	98.30 68	91.90 49	99.85 18	95.61 96	99.68 4	99.54 36

test_fmvsmconf0.1_n		97.09 28	97.06 23	97.19 67	95.67 256	92.21 102	97.95 72	98.27 41	95.78 15	98.40 28	99.00 10	89.99 84	99.78 39	99.06 9	99.41 53	99.59 24

SED-MVS		98.05 2	97.99 1	98.24 10	99.42 7	95.30 17	98.25 35	98.27 41	95.13 29	99.19 6	98.89 19	95.54 5	99.85 18	97.52 31	99.66 10	99.56 31

test_241102_TWO								98.27 41	95.13 29	98.93 12	98.89 19	94.99 11	99.85 18	97.52 31	99.65 13	99.74 8

test_241102_ONE					99.42 7	95.30 17		98.27 41	95.09 32	99.19 6	98.81 27	95.54 5	99.65 64

SF-MVS		97.39 18	97.13 20	98.17 15	99.02 42	95.28 19	98.23 39	98.27 41	92.37 138	98.27 30	98.65 34	93.33 23	99.72 51	96.49 58	99.52 30	99.51 40

SteuartSystems-ACMMP		97.62 9	97.53 11	97.87 24	98.39 80	94.25 40	98.43 22	98.27 41	95.34 23	98.11 32	98.56 36	94.53 12	99.71 52	96.57 56	99.62 17	99.65 17
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test_one_0601					99.32 22	95.20 20		98.25 47	95.13 29	98.48 27	98.87 21	95.16 7

PVSNet_Blended_VisFu		95.27 98	94.91 102	96.38 106	98.20 96	90.86 158	97.27 161	98.25 47	90.21 212	94.18 155	97.27 144	87.48 125	99.73 48	93.53 141	97.77 142	98.55 137

region2R		97.07 30	96.84 38	97.77 33	99.46 2	93.79 54	98.52 15	98.24 49	93.19 109	97.14 61	98.34 60	91.59 56	99.87 7	95.46 100	99.59 19	99.64 18

PS-CasMVS		91.55 237	90.84 237	93.69 261	94.96 299	88.28 243	97.84 85	98.24 49	91.46 164	88.04 314	95.80 225	79.67 259	97.48 333	87.02 279	84.54 350	95.31 299

DU-MVS		92.90 183	92.04 190	95.49 164	94.95 300	92.83 81	97.16 173	98.24 49	93.02 117	90.13 251	95.71 232	83.47 182	97.85 298	91.71 179	83.93 356	95.78 272

9.14			96.75 47		98.93 50		97.73 100	98.23 52	91.28 173	97.88 40	98.44 49	93.00 26	99.65 64	95.76 87	99.47 40

reproduce_model		97.51 14	97.51 13	97.50 49	98.99 46	93.01 77	97.79 94	98.21 53	95.73 16	97.99 36	99.03 9	92.63 36	99.82 28	97.80 21	99.42 50	99.67 13

D2MVS		91.30 253	90.95 231	92.35 306	94.71 315	85.52 307	96.18 258	98.21 53	88.89 255	86.60 342	93.82 325	79.92 255	97.95 288	89.29 229	90.95 277	93.56 363

reproduce-ours		97.53 12	97.51 13	97.60 46	98.97 47	93.31 68	97.71 105	98.20 55	95.80 13	97.88 40	98.98 12	92.91 27	99.81 30	97.68 23	99.43 48	99.67 13

our_new_method		97.53 12	97.51 13	97.60 46	98.97 47	93.31 68	97.71 105	98.20 55	95.80 13	97.88 40	98.98 12	92.91 27	99.81 30	97.68 23	99.43 48	99.67 13

SDMVSNet		94.17 129	93.61 134	95.86 141	98.09 104	91.37 135	97.35 153	98.20 55	93.18 111	91.79 212	97.28 142	79.13 267	98.93 175	94.61 123	92.84 243	97.28 224

XVS		97.18 24	96.96 32	97.81 28	99.38 14	94.03 50	98.59 12	98.20 55	94.85 40	96.59 84	98.29 69	91.70 52	99.80 33	95.66 89	99.40 55	99.62 20

X-MVStestdata		91.71 226	89.67 290	97.81 28	99.38 14	94.03 50	98.59 12	98.20 55	94.85 40	96.59 84	32.69 423	91.70 52	99.80 33	95.66 89	99.40 55	99.62 20

ACMMP_NAP		97.20 23	96.86 36	98.23 11	99.09 34	95.16 22	97.60 121	98.19 60	92.82 129	97.93 39	98.74 31	91.60 55	99.86 9	96.26 61	99.52 30	99.67 13

CP-MVSNet		91.89 222	91.24 221	93.82 253	95.05 296	88.57 234	97.82 90	98.19 60	91.70 157	88.21 310	95.76 230	81.96 219	97.52 331	87.86 254	84.65 344	95.37 295

ZNCC-MVS		96.96 34	96.67 50	97.85 25	99.37 16	94.12 46	98.49 19	98.18 62	92.64 134	96.39 94	98.18 76	91.61 54	99.88 4	95.59 99	99.55 26	99.57 28

SMA-MVS		97.35 19	97.03 28	98.30 8	99.06 38	95.42 10	97.94 73	98.18 62	90.57 204	98.85 18	98.94 15	93.33 23	99.83 26	96.72 51	99.68 4	99.63 19
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		91.20 258	90.44 254	93.48 270	94.49 323	87.91 257	97.76 96	98.18 62	91.29 170	87.78 318	95.74 231	80.35 246	97.33 344	85.46 303	82.96 366	95.19 310

DELS-MVS		96.61 58	96.38 66	97.30 57	97.79 127	93.19 73	95.96 268	98.18 62	95.23 25	95.87 113	97.65 119	91.45 57	99.70 57	95.87 81	99.44 47	99.00 96
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		89.70 308	88.40 314	93.60 264	95.15 291	90.10 181	97.56 125	98.16 66	87.28 307	86.16 346	94.63 282	77.57 295	98.05 268	74.48 385	84.59 348	92.65 376

VNet		95.89 82	95.45 85	97.21 65	98.07 108	92.94 80	97.50 133	98.15 67	93.87 81	97.52 47	97.61 125	85.29 154	99.53 97	95.81 86	95.27 199	99.16 76

DeepPCF-MVS	93.97 1	96.61 58	97.09 22	95.15 176	98.09 104	86.63 287	96.00 266	98.15 67	95.43 20	97.95 38	98.56 36	93.40 21	99.36 120	96.77 48	99.48 39	99.45 50

SD-MVS		97.41 17	97.53 11	97.06 73	98.57 72	94.46 34	97.92 75	98.14 69	94.82 44	99.01 9	98.55 38	94.18 14	97.41 340	96.94 44	99.64 14	99.32 65
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 42	96.52 56	97.82 27	99.36 18	94.14 45	98.29 29	98.13 70	92.72 131	96.70 76	98.06 83	91.35 61	99.86 9	94.83 114	99.28 66	99.47 49

UA-Net		95.95 80	95.53 81	97.20 66	97.67 132	92.98 79	97.65 112	98.13 70	94.81 45	96.61 82	98.35 57	88.87 95	99.51 102	90.36 204	97.35 154	99.11 84

QAPM		93.45 159	92.27 185	96.98 76	96.77 190	92.62 87	98.39 24	98.12 72	84.50 352	88.27 308	97.77 110	82.39 212	99.81 30	85.40 304	98.81 100	98.51 142

Vis-MVSNet		95.23 100	94.81 103	96.51 93	97.18 157	91.58 126	98.26 34	98.12 72	94.38 69	94.90 137	98.15 78	82.28 213	98.92 176	91.45 186	98.58 111	99.01 93
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVS	89.19 12	92.86 185	91.68 204	96.40 103	95.34 275	92.73 85	98.27 32	98.12 72	84.86 347	85.78 348	97.75 111	78.89 277	99.74 46	87.50 269	98.65 106	96.73 240

TranMVSNet+NR-MVSNet		92.50 194	91.63 205	95.14 177	94.76 311	92.07 107	97.53 130	98.11 75	92.90 127	89.56 272	96.12 209	83.16 189	97.60 323	89.30 228	83.20 365	95.75 276

CPTT-MVS		95.57 92	95.19 95	96.70 78	99.27 26	91.48 130	98.33 26	98.11 75	87.79 292	95.17 133	98.03 86	87.09 132	99.61 75	93.51 142	99.42 50	99.02 90

APD-MVS		96.95 35	96.60 52	98.01 20	99.03 41	94.93 27	97.72 103	98.10 77	91.50 162	98.01 35	98.32 65	92.33 42	99.58 83	94.85 112	99.51 33	99.53 39
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mPP-MVS		96.86 40	96.60 52	97.64 44	99.40 11	93.44 61	98.50 18	98.09 78	93.27 105	95.95 112	98.33 63	91.04 69	99.88 4	95.20 103	99.57 25	99.60 23

ZD-MVS					99.05 39	94.59 32		98.08 79	89.22 242	97.03 66	98.10 79	92.52 39	99.65 64	94.58 124	99.31 65

MTGPA								98.08 79

MTAPA		97.08 29	96.78 45	97.97 23	99.37 16	94.42 36	97.24 163	98.08 79	95.07 33	96.11 104	98.59 35	90.88 74	99.90 2	96.18 73	99.50 35	99.58 27

CNVR-MVS		97.68 6	97.44 16	98.37 7	98.90 53	95.86 6	97.27 161	98.08 79	95.81 12	97.87 43	98.31 66	94.26 13	99.68 60	97.02 43	99.49 38	99.57 28

DP-MVS Recon		95.68 87	95.12 99	97.37 54	99.19 31	94.19 42	97.03 180	98.08 79	88.35 275	95.09 135	97.65 119	89.97 85	99.48 107	92.08 171	98.59 110	98.44 153

SR-MVS		97.01 33	96.86 36	97.47 51	99.09 34	93.27 70	97.98 63	98.07 84	93.75 84	97.45 49	98.48 46	91.43 59	99.59 80	96.22 64	99.27 67	99.54 36

MCST-MVS		97.18 24	96.84 38	98.20 14	99.30 24	95.35 15	97.12 176	98.07 84	93.54 94	96.08 106	97.69 114	93.86 16	99.71 52	96.50 57	99.39 57	99.55 34

NR-MVSNet		92.34 202	91.27 220	95.53 161	94.95 300	93.05 76	97.39 149	98.07 84	92.65 133	84.46 359	95.71 232	85.00 158	97.77 308	89.71 216	83.52 362	95.78 272

MP-MVS-pluss		96.70 52	96.27 69	97.98 22	99.23 30	94.71 29	96.96 190	98.06 87	90.67 195	95.55 125	98.78 30	91.07 68	99.86 9	96.58 55	99.55 26	99.38 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
APD-MVS_3200maxsize		96.81 46	96.71 49	97.12 69	99.01 45	92.31 98	97.98 63	98.06 87	93.11 115	97.44 50	98.55 38	90.93 72	99.55 93	96.06 74	99.25 71	99.51 40

MP-MVS		96.77 48	96.45 63	97.72 38	99.39 13	93.80 53	98.41 23	98.06 87	93.37 101	95.54 127	98.34 60	90.59 78	99.88 4	94.83 114	99.54 28	99.49 45
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS_fast		96.51 61	96.27 69	97.22 64	99.32 22	92.74 84	98.74 9	98.06 87	90.57 204	96.77 73	98.35 57	90.21 81	99.53 97	94.80 117	99.63 16	99.38 61

HPM-MVS		96.69 54	96.45 63	97.40 53	99.36 18	93.11 75	98.87 6	98.06 87	91.17 178	96.40 93	97.99 90	90.99 70	99.58 83	95.61 96	99.61 18	99.49 45
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
sss		94.51 121	93.80 129	96.64 80	97.07 163	91.97 111	96.32 247	98.06 87	88.94 253	94.50 147	96.78 168	84.60 162	99.27 129	91.90 172	96.02 182	98.68 129

DeepC-MVS	93.07 3	96.06 74	95.66 79	97.29 58	97.96 116	93.17 74	97.30 159	98.06 87	93.92 79	93.38 174	98.66 32	86.83 134	99.73 48	95.60 98	99.22 73	98.96 98
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 21	97.03 28	98.11 17	98.77 56	95.06 25	97.34 154	98.04 94	95.96 9	97.09 64	97.88 98	93.18 25	99.71 52	95.84 85	99.17 79	99.56 31

DeepC-MVS_fast	93.89 2	96.93 37	96.64 51	97.78 31	98.64 67	94.30 37	97.41 144	98.04 94	94.81 45	96.59 84	98.37 55	91.24 64	99.64 72	95.16 105	99.52 30	99.42 56
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 39	96.80 44	97.11 70	99.02 42	92.34 96	97.98 63	98.03 96	93.52 96	97.43 52	98.51 41	91.40 60	99.56 91	96.05 75	99.26 69	99.43 54

RE-MVS-def			96.72 48		99.02 42	92.34 96	97.98 63	98.03 96	93.52 96	97.43 52	98.51 41	90.71 76		96.05 75	99.26 69	99.43 54

RPMNet		88.98 314	87.05 328	94.77 202	94.45 325	87.19 272	90.23 398	98.03 96	77.87 399	92.40 191	87.55 403	80.17 250	99.51 102	68.84 404	93.95 230	97.60 209

save fliter					98.91 52	94.28 38	97.02 182	98.02 99	95.35 22

TEST9					98.70 59	94.19 42	96.41 236	98.02 99	88.17 279	96.03 107	97.56 129	92.74 33	99.59 80

train_agg		96.30 70	95.83 78	97.72 38	98.70 59	94.19 42	96.41 236	98.02 99	88.58 266	96.03 107	97.56 129	92.73 34	99.59 80	95.04 107	99.37 61	99.39 59

test_8					98.67 61	94.06 49	96.37 243	98.01 102	88.58 266	95.98 111	97.55 131	92.73 34	99.58 83

agg_prior					98.67 61	93.79 54		98.00 103		95.68 121			99.57 90

test_prior				97.23 63	98.67 61	92.99 78		98.00 103					99.41 115			99.29 66

WR-MVS		92.34 202	91.53 209	94.77 202	95.13 293	90.83 159	96.40 240	97.98 105	91.88 153	89.29 281	95.54 243	82.50 208	97.80 304	89.79 215	85.27 335	95.69 279

HPM-MVS++		97.34 20	96.97 31	98.47 5	99.08 36	96.16 4	97.55 129	97.97 106	95.59 17	96.61 82	97.89 96	92.57 38	99.84 23	95.95 80	99.51 33	99.40 57

CANet		96.39 66	96.02 73	97.50 49	97.62 139	93.38 63	97.02 182	97.96 107	95.42 21	94.86 138	97.81 107	87.38 128	99.82 28	96.88 46	99.20 76	99.29 66

114514_t		93.95 141	93.06 154	96.63 82	99.07 37	91.61 123	97.46 142	97.96 107	77.99 397	93.00 182	97.57 127	86.14 146	99.33 121	89.22 232	99.15 82	98.94 101

IU-MVS					99.42 7	95.39 11		97.94 109	90.40 210	98.94 11				97.41 38	99.66 10	99.74 8

MSC_two_6792asdad				98.86 1	98.67 61	96.94 1		97.93 110					99.86 9	97.68 23	99.67 6	99.77 2

No_MVS				98.86 1	98.67 61	96.94 1		97.93 110					99.86 9	97.68 23	99.67 6	99.77 2

fmvsm_s_conf0.1_n_2		96.33 69	96.44 65	96.00 135	97.30 152	90.37 177	97.53 130	97.92 112	96.52 5	99.14 8	99.08 4	83.21 187	99.74 46	99.22 6	98.06 133	97.88 190

Anonymous20231211		90.63 282	89.42 297	94.27 229	98.24 90	89.19 221	98.05 57	97.89 113	79.95 389	88.25 309	94.96 264	72.56 333	98.13 251	89.70 217	85.14 337	95.49 283

原ACMM1				96.38 106	98.59 69	91.09 151		97.89 113	87.41 303	95.22 132	97.68 115	90.25 80	99.54 95	87.95 253	99.12 86	98.49 145

CDPH-MVS		95.97 79	95.38 90	97.77 33	98.93 50	94.44 35	96.35 244	97.88 115	86.98 311	96.65 80	97.89 96	91.99 48	99.47 108	92.26 162	99.46 41	99.39 59

test11								97.88 115

EIA-MVS		95.53 93	95.47 84	95.71 151	97.06 166	89.63 195	97.82 90	97.87 117	93.57 90	93.92 162	95.04 262	90.61 77	98.95 172	94.62 122	98.68 105	98.54 138

CS-MVS		96.86 40	97.06 23	96.26 116	98.16 101	91.16 149	99.09 3	97.87 117	95.30 24	97.06 65	98.03 86	91.72 50	98.71 202	97.10 41	99.17 79	98.90 108

无先验							95.79 278	97.87 117	83.87 360				99.65 64	87.68 263		98.89 112

3Dnovator+	91.43 4	95.40 94	94.48 117	98.16 16	96.90 176	95.34 16	98.48 20	97.87 117	94.65 55	88.53 300	98.02 88	83.69 178	99.71 52	93.18 149	98.96 95	99.44 52

VPNet		92.23 210	91.31 217	94.99 185	95.56 260	90.96 154	97.22 168	97.86 121	92.96 124	90.96 234	96.62 185	75.06 315	98.20 245	91.90 172	83.65 361	95.80 270

test_vis1_n_1920		94.17 129	94.58 110	92.91 290	97.42 150	82.02 357	97.83 88	97.85 122	94.68 52	98.10 33	98.49 43	70.15 351	99.32 123	97.91 20	98.82 99	97.40 218

DVP-MVS		97.91 3	97.81 4	98.22 13	99.45 3	95.36 13	98.21 42	97.85 122	94.92 38	98.73 21	98.87 21	95.08 8	99.84 23	97.52 31	99.67 6	99.48 47
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 16	97.33 19	97.69 41	99.25 27	94.24 41	98.07 55	97.85 122	93.72 85	98.57 24	98.35 57	93.69 18	99.40 116	97.06 42	99.46 41	99.44 52
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SPE-MVS-test		96.89 38	97.04 27	96.45 100	98.29 85	91.66 122	99.03 4	97.85 122	95.84 10	96.90 68	97.97 92	91.24 64	98.75 195	96.92 45	99.33 63	98.94 101

test_fmvsmconf0.01_n		96.15 73	95.85 77	97.03 74	92.66 372	91.83 115	97.97 69	97.84 126	95.57 18	97.53 46	99.00 10	84.20 171	99.76 42	98.82 14	99.08 88	99.48 47

GDP-MVS		95.62 89	95.13 97	97.09 71	96.79 187	93.26 71	97.89 79	97.83 127	93.58 89	96.80 70	97.82 106	83.06 194	99.16 143	94.40 126	97.95 137	98.87 114

balanced_conf03		96.84 44	96.89 35	96.68 79	97.63 138	92.22 101	98.17 48	97.82 128	94.44 64	98.23 31	97.36 139	90.97 71	99.22 133	97.74 22	99.66 10	98.61 132

AdaColmap		94.34 125	93.68 132	96.31 110	98.59 69	91.68 121	96.59 227	97.81 129	89.87 220	92.15 201	97.06 156	83.62 181	99.54 95	89.34 227	98.07 132	97.70 202

MVSMamba_PlusPlus		96.51 61	96.48 58	96.59 85	98.07 108	91.97 111	98.14 49	97.79 130	90.43 208	97.34 55	97.52 132	91.29 63	99.19 136	98.12 18	99.64 14	98.60 133

mamv4		94.66 119	96.10 72	90.37 355	98.01 111	73.41 403	96.82 201	97.78 131	89.95 219	94.52 146	97.43 136	92.91 27	99.09 155	98.28 17	99.16 81	98.60 133

ETV-MVS		96.02 76	95.89 76	96.40 103	97.16 158	92.44 93	97.47 140	97.77 132	94.55 58	96.48 89	94.51 288	91.23 66	98.92 176	95.65 92	98.19 127	97.82 197

新几何1				97.32 56	98.60 68	93.59 58		97.75 133	81.58 380	95.75 118	97.85 102	90.04 83	99.67 62	86.50 285	99.13 84	98.69 128

旧先验1					98.38 81	93.38 63		97.75 133			98.09 81	92.30 45			99.01 93	99.16 76

EC-MVSNet		96.42 64	96.47 59	96.26 116	97.01 172	91.52 128	98.89 5	97.75 133	94.42 65	96.64 81	97.68 115	89.32 89	98.60 212	97.45 35	99.11 87	98.67 130

EI-MVSNet-Vis-set		96.51 61	96.47 59	96.63 82	98.24 90	91.20 143	96.89 194	97.73 136	94.74 50	96.49 88	98.49 43	90.88 74	99.58 83	96.44 59	98.32 122	99.13 80

PAPM_NR		95.01 105	94.59 109	96.26 116	98.89 54	90.68 166	97.24 163	97.73 136	91.80 154	92.93 187	96.62 185	89.13 92	99.14 148	89.21 233	97.78 141	98.97 97

Anonymous20240529		91.98 218	90.73 244	95.73 149	98.14 102	89.40 208	97.99 62	97.72 138	79.63 391	93.54 169	97.41 137	69.94 353	99.56 91	91.04 194	91.11 273	98.22 166

CHOSEN 280x420		93.12 171	92.72 169	94.34 223	96.71 194	87.27 268	90.29 397	97.72 138	86.61 318	91.34 223	95.29 250	84.29 170	98.41 226	93.25 148	98.94 96	97.35 221

EI-MVSNet-UG-set		96.34 68	96.30 68	96.47 97	98.20 96	90.93 156	96.86 196	97.72 138	94.67 53	96.16 103	98.46 47	90.43 79	99.58 83	96.23 63	97.96 136	98.90 108

LS3D		93.57 155	92.61 173	96.47 97	97.59 142	91.61 123	97.67 109	97.72 138	85.17 342	90.29 245	98.34 60	84.60 162	99.73 48	83.85 325	98.27 124	98.06 181

PAPR		94.18 128	93.42 147	96.48 96	97.64 136	91.42 134	95.55 290	97.71 142	88.99 250	92.34 197	95.82 224	89.19 90	99.11 151	86.14 291	97.38 152	98.90 108

UGNet		94.04 139	93.28 150	96.31 110	96.85 179	91.19 144	97.88 80	97.68 143	94.40 67	93.00 182	96.18 204	73.39 330	99.61 75	91.72 178	98.46 116	98.13 174
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
testdata				95.46 168	98.18 100	88.90 227		97.66 144	82.73 371	97.03 66	98.07 82	90.06 82	98.85 183	89.67 218	98.98 94	98.64 131

test12				97.65 42	98.46 73	94.26 39		97.66 144		95.52 128		90.89 73	99.46 109		99.25 71	99.22 73

DTE-MVSNet		90.56 283	89.75 288	93.01 286	93.95 338	87.25 269	97.64 116	97.65 146	90.74 190	87.12 330	95.68 235	79.97 254	97.00 356	83.33 326	81.66 372	94.78 337

TAPA-MVS	90.10 7	92.30 205	91.22 223	95.56 158	98.33 83	89.60 197	96.79 203	97.65 146	81.83 377	91.52 218	97.23 147	87.94 111	98.91 178	71.31 399	98.37 120	98.17 172
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sd_testset		93.10 172	92.45 181	95.05 181	98.09 104	89.21 218	96.89 194	97.64 148	93.18 111	91.79 212	97.28 142	75.35 314	98.65 207	88.99 238	92.84 243	97.28 224

test_cas_vis1_n_1920		94.48 123	94.55 114	94.28 228	96.78 188	86.45 292	97.63 118	97.64 148	93.32 104	97.68 45	98.36 56	73.75 328	99.08 158	96.73 50	99.05 90	97.31 223

cdsmvs_eth3d_5k		23.24 393	30.99 395	0.00 411	0.00 434	0.00 436	0.00 422	97.63 150	0.00 429	0.00 430	96.88 165	84.38 167	0.00 430	0.00 429	0.00 428	0.00 426

DPM-MVS		95.69 86	94.92 101	98.01 20	98.08 107	95.71 9	95.27 305	97.62 151	90.43 208	95.55 125	97.07 155	91.72 50	99.50 105	89.62 220	98.94 96	98.82 120

sasdasda		96.02 76	95.45 85	97.75 35	97.59 142	95.15 23	98.28 30	97.60 152	94.52 60	96.27 98	96.12 209	87.65 117	99.18 139	96.20 69	94.82 208	98.91 105

canonicalmvs		96.02 76	95.45 85	97.75 35	97.59 142	95.15 23	98.28 30	97.60 152	94.52 60	96.27 98	96.12 209	87.65 117	99.18 139	96.20 69	94.82 208	98.91 105

test222					98.24 90	92.21 102	95.33 300	97.60 152	79.22 393	95.25 130	97.84 104	88.80 97			99.15 82	98.72 125

cascas		91.20 258	90.08 271	94.58 211	94.97 298	89.16 222	93.65 362	97.59 155	79.90 390	89.40 276	92.92 350	75.36 313	98.36 233	92.14 167	94.75 211	96.23 250

h-mvs33		94.15 131	93.52 140	96.04 129	97.81 126	90.22 180	97.62 120	97.58 156	95.19 26	96.74 74	97.45 133	83.67 179	99.61 75	95.85 83	79.73 379	98.29 163

MGCFI-Net		95.94 81	95.40 89	97.56 48	97.59 142	94.62 31	98.21 42	97.57 157	94.41 66	96.17 102	96.16 207	87.54 121	99.17 141	96.19 71	94.73 213	98.91 105

MVSFormer		95.37 95	95.16 96	95.99 136	96.34 225	91.21 141	98.22 40	97.57 157	91.42 166	96.22 100	97.32 140	86.20 144	97.92 292	94.07 130	99.05 90	98.85 116

test_djsdf		93.07 174	92.76 164	94.00 240	93.49 354	88.70 231	98.22 40	97.57 157	91.42 166	90.08 257	95.55 242	82.85 200	97.92 292	94.07 130	91.58 264	95.40 292

OMC-MVS		95.09 104	94.70 107	96.25 119	98.46 73	91.28 137	96.43 234	97.57 157	92.04 149	94.77 141	97.96 93	87.01 133	99.09 155	91.31 188	96.77 169	98.36 160

PS-MVSNAJss		93.74 150	93.51 141	94.44 217	93.91 340	89.28 216	97.75 97	97.56 161	92.50 135	89.94 259	96.54 188	88.65 100	98.18 248	93.83 139	90.90 278	95.86 264

casdiffmvs_mvg		95.81 85	95.57 80	96.51 93	96.87 177	91.49 129	97.50 133	97.56 161	93.99 77	95.13 134	97.92 95	87.89 112	98.78 190	95.97 79	97.33 155	99.26 70
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		92.42 198	91.89 197	94.03 239	93.33 360	88.50 238	97.73 100	97.53 163	92.00 151	88.85 292	96.50 190	75.62 312	98.11 255	93.88 137	91.56 265	95.48 284

mvs_tets		92.31 204	91.76 200	93.94 247	93.41 357	88.29 242	97.63 118	97.53 163	92.04 149	88.76 295	96.45 192	74.62 320	98.09 260	93.91 135	91.48 266	95.45 288

dcpmvs_2		96.37 67	97.05 26	94.31 226	98.96 49	84.11 332	97.56 125	97.51 165	93.92 79	97.43 52	98.52 40	92.75 32	99.32 123	97.32 40	99.50 35	99.51 40

HQP_MVS		93.78 149	93.43 145	94.82 195	96.21 229	89.99 185	97.74 98	97.51 165	94.85 40	91.34 223	96.64 178	81.32 229	98.60 212	93.02 155	92.23 252	95.86 264

plane_prior5								97.51 165					98.60 212	93.02 155	92.23 252	95.86 264

reproduce_monomvs		91.30 253	91.10 227	91.92 317	96.82 184	82.48 351	97.01 185	97.49 168	94.64 56	88.35 303	95.27 253	70.53 346	98.10 256	95.20 103	84.60 347	95.19 310

PS-MVSNAJ		95.37 95	95.33 92	95.49 164	97.35 151	90.66 167	95.31 302	97.48 169	93.85 82	96.51 87	95.70 234	88.65 100	99.65 64	94.80 117	98.27 124	96.17 254

API-MVS		94.84 114	94.49 116	95.90 138	97.90 122	92.00 110	97.80 93	97.48 169	89.19 243	94.81 139	96.71 171	88.84 96	99.17 141	88.91 240	98.76 103	96.53 243

MG-MVS		95.61 90	95.38 90	96.31 110	98.42 76	90.53 169	96.04 263	97.48 169	93.47 98	95.67 122	98.10 79	89.17 91	99.25 130	91.27 189	98.77 102	99.13 80

MAR-MVS		94.22 127	93.46 143	96.51 93	98.00 113	92.19 105	97.67 109	97.47 172	88.13 282	93.00 182	95.84 222	84.86 160	99.51 102	87.99 252	98.17 129	97.83 196
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		92.98 178	92.53 177	94.32 224	96.12 239	89.20 219	95.28 303	97.47 172	92.66 132	89.90 260	95.62 238	80.58 241	98.40 227	92.73 160	92.40 250	95.38 294
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_ETH3D		91.34 251	90.22 267	94.68 205	94.86 307	87.86 258	97.23 167	97.46 174	87.99 283	89.90 260	96.92 163	66.35 378	98.23 242	90.30 205	90.99 276	97.96 185

nrg030		94.05 138	93.31 149	96.27 115	95.22 286	94.59 32	98.34 25	97.46 174	92.93 125	91.21 232	96.64 178	87.23 131	98.22 243	94.99 110	85.80 327	95.98 263

XVG-OURS		93.72 151	93.35 148	94.80 200	97.07 163	88.61 232	94.79 320	97.46 174	91.97 152	93.99 159	97.86 101	81.74 224	98.88 180	92.64 161	92.67 248	96.92 235

LPG-MVS_test		92.94 181	92.56 174	94.10 234	96.16 234	88.26 244	97.65 112	97.46 174	91.29 170	90.12 253	97.16 150	79.05 270	98.73 198	92.25 164	91.89 260	95.31 299

LGP-MVS_train				94.10 234	96.16 234	88.26 244		97.46 174	91.29 170	90.12 253	97.16 150	79.05 270	98.73 198	92.25 164	91.89 260	95.31 299

MVS		91.71 226	90.44 254	95.51 162	95.20 288	91.59 125	96.04 263	97.45 179	73.44 407	87.36 327	95.60 239	85.42 153	99.10 152	85.97 296	97.46 147	95.83 268

XVG-OURS-SEG-HR		93.86 146	93.55 136	94.81 197	97.06 166	88.53 237	95.28 303	97.45 179	91.68 158	94.08 158	97.68 115	82.41 211	98.90 179	93.84 138	92.47 249	96.98 231

baseline		95.58 91	95.42 88	96.08 125	96.78 188	90.41 175	97.16 173	97.45 179	93.69 88	95.65 123	97.85 102	87.29 129	98.68 204	95.66 89	97.25 160	99.13 80

ab-mvs		93.57 155	92.55 175	96.64 80	97.28 153	91.96 113	95.40 297	97.45 179	89.81 225	93.22 180	96.28 200	79.62 261	99.46 109	90.74 198	93.11 240	98.50 143

xiu_mvs_v2_base		95.32 97	95.29 93	95.40 169	97.22 154	90.50 170	95.44 296	97.44 183	93.70 87	96.46 91	96.18 204	88.59 103	99.53 97	94.79 119	97.81 140	96.17 254

1314		92.81 189	92.03 191	95.14 177	95.33 278	89.52 203	96.04 263	97.44 183	87.72 296	86.25 345	95.33 249	83.84 176	98.79 189	89.26 230	97.05 165	97.11 229

casdiffmvs		95.64 88	95.49 82	96.08 125	96.76 193	90.45 172	97.29 160	97.44 183	94.00 76	95.46 129	97.98 91	87.52 124	98.73 198	95.64 93	97.33 155	99.08 87
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		92.16 212	91.23 222	94.95 191	94.75 312	90.94 155	97.47 140	97.43 186	89.14 244	88.90 289	96.43 193	79.71 258	98.24 241	89.56 221	87.68 309	95.67 280

anonymousdsp		92.16 212	91.55 208	93.97 243	92.58 374	89.55 200	97.51 132	97.42 187	89.42 237	88.40 302	94.84 271	80.66 240	97.88 297	91.87 174	91.28 270	94.48 345

Effi-MVS+		94.93 110	94.45 118	96.36 108	96.61 198	91.47 131	96.41 236	97.41 188	91.02 184	94.50 147	95.92 218	87.53 122	98.78 190	93.89 136	96.81 168	98.84 119

RRT-MVS		94.51 121	94.35 121	94.98 187	96.40 221	86.55 290	97.56 125	97.41 188	93.19 109	94.93 136	97.04 157	79.12 268	99.30 127	96.19 71	97.32 157	99.09 86

HQP3-MVS								97.39 190							92.10 257

HQP-MVS		93.19 168	92.74 167	94.54 213	95.86 246	89.33 212	96.65 218	97.39 190	93.55 91	90.14 247	95.87 220	80.95 233	98.50 220	92.13 168	92.10 257	95.78 272

PLC	91.00 6	94.11 135	93.43 145	96.13 124	98.58 71	91.15 150	96.69 214	97.39 190	87.29 306	91.37 222	96.71 171	88.39 104	99.52 101	87.33 272	97.13 164	97.73 200
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7n		90.76 275	89.86 281	93.45 272	93.54 351	87.60 264	97.70 108	97.37 193	88.85 256	87.65 320	94.08 316	81.08 232	98.10 256	84.68 312	83.79 360	94.66 342

UnsupCasMVSNet_eth		85.99 348	84.45 353	90.62 351	89.97 392	82.40 354	93.62 363	97.37 193	89.86 221	78.59 394	92.37 360	65.25 386	95.35 386	82.27 339	70.75 402	94.10 356

ACMM	89.79 8	92.96 179	92.50 179	94.35 221	96.30 227	88.71 230	97.58 122	97.36 195	91.40 168	90.53 240	96.65 177	79.77 257	98.75 195	91.24 190	91.64 262	95.59 282
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
xiu_mvs_v1_base_debu		95.01 105	94.76 104	95.75 146	96.58 201	91.71 118	96.25 252	97.35 196	92.99 118	96.70 76	96.63 182	82.67 203	99.44 112	96.22 64	97.46 147	96.11 259

xiu_mvs_v1_base		95.01 105	94.76 104	95.75 146	96.58 201	91.71 118	96.25 252	97.35 196	92.99 118	96.70 76	96.63 182	82.67 203	99.44 112	96.22 64	97.46 147	96.11 259

xiu_mvs_v1_base_debi		95.01 105	94.76 104	95.75 146	96.58 201	91.71 118	96.25 252	97.35 196	92.99 118	96.70 76	96.63 182	82.67 203	99.44 112	96.22 64	97.46 147	96.11 259

diffmvs		95.25 99	95.13 97	95.63 154	96.43 220	89.34 211	95.99 267	97.35 196	92.83 128	96.31 96	97.37 138	86.44 139	98.67 205	96.26 61	97.19 162	98.87 114
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
WTY-MVS		94.71 118	94.02 125	96.79 77	97.71 131	92.05 108	96.59 227	97.35 196	90.61 201	94.64 143	96.93 160	86.41 140	99.39 117	91.20 191	94.71 214	98.94 101

F-COLMAP		93.58 154	92.98 156	95.37 170	98.40 78	88.98 225	97.18 171	97.29 201	87.75 295	90.49 241	97.10 154	85.21 155	99.50 105	86.70 282	96.72 172	97.63 204

XVG-ACMP-BASELINE		90.93 271	90.21 268	93.09 284	94.31 331	85.89 302	95.33 300	97.26 202	91.06 183	89.38 277	95.44 247	68.61 361	98.60 212	89.46 223	91.05 274	94.79 335

PCF-MVS	89.48 11	91.56 236	89.95 278	96.36 108	96.60 199	92.52 91	92.51 382	97.26 202	79.41 392	88.90 289	96.56 187	84.04 175	99.55 93	77.01 376	97.30 158	97.01 230
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ACMP	89.59 10	92.62 193	92.14 188	94.05 237	96.40 221	88.20 247	97.36 152	97.25 204	91.52 161	88.30 306	96.64 178	78.46 282	98.72 201	91.86 175	91.48 266	95.23 306
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OPM-MVS		93.28 164	92.76 164	94.82 195	94.63 318	90.77 162	96.65 218	97.18 205	93.72 85	91.68 216	97.26 145	79.33 265	98.63 209	92.13 168	92.28 251	95.07 313
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PatchMatch-RL		92.90 183	92.02 192	95.56 158	98.19 98	90.80 160	95.27 305	97.18 205	87.96 284	91.86 211	95.68 235	80.44 244	98.99 170	84.01 320	97.54 146	96.89 236

alignmvs		95.87 84	95.23 94	97.78 31	97.56 147	95.19 21	97.86 81	97.17 207	94.39 68	96.47 90	96.40 195	85.89 147	99.20 135	96.21 68	95.11 204	98.95 100

MVS_Test		94.89 112	94.62 108	95.68 152	96.83 182	89.55 200	96.70 212	97.17 207	91.17 178	95.60 124	96.11 213	87.87 114	98.76 194	93.01 157	97.17 163	98.72 125

Fast-Effi-MVS+		93.46 158	92.75 166	95.59 157	96.77 190	90.03 182	96.81 202	97.13 209	88.19 278	91.30 226	94.27 305	86.21 143	98.63 209	87.66 264	96.46 179	98.12 175

EI-MVSNet		93.03 176	92.88 160	93.48 270	95.77 252	86.98 277	96.44 232	97.12 210	90.66 197	91.30 226	97.64 122	86.56 136	98.05 268	89.91 211	90.55 282	95.41 289

MVSTER		93.20 167	92.81 163	94.37 220	96.56 204	89.59 198	97.06 179	97.12 210	91.24 174	91.30 226	95.96 216	82.02 218	98.05 268	93.48 143	90.55 282	95.47 286

test_yl		94.78 116	94.23 122	96.43 101	97.74 129	91.22 139	96.85 197	97.10 212	91.23 175	95.71 119	96.93 160	84.30 168	99.31 125	93.10 150	95.12 202	98.75 122

DCV-MVSNet		94.78 116	94.23 122	96.43 101	97.74 129	91.22 139	96.85 197	97.10 212	91.23 175	95.71 119	96.93 160	84.30 168	99.31 125	93.10 150	95.12 202	98.75 122

LTVRE_ROB	88.41 13	90.99 267	89.92 280	94.19 230	96.18 232	89.55 200	96.31 248	97.09 214	87.88 287	85.67 349	95.91 219	78.79 278	98.57 216	81.50 342	89.98 287	94.44 348
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		92.73 191	92.88 160	92.29 309	96.08 242	81.05 365	97.98 63	97.08 215	90.72 192	96.79 72	98.18 76	63.07 390	98.45 224	97.62 29	98.42 119	97.36 219

v10		91.04 265	90.23 265	93.49 269	94.12 334	88.16 250	97.32 157	97.08 215	88.26 277	88.29 307	94.22 310	82.17 216	97.97 280	86.45 286	84.12 354	94.33 351

v144192		91.06 264	90.28 261	93.39 273	93.66 349	87.23 271	96.83 200	97.07 217	87.43 302	89.69 267	94.28 304	81.48 227	98.00 275	87.18 276	84.92 343	94.93 321

v1192		91.07 263	90.23 265	93.58 266	93.70 346	87.82 260	96.73 208	97.07 217	87.77 293	89.58 270	94.32 302	80.90 237	97.97 280	86.52 284	85.48 330	94.95 317

v8		91.29 255	90.53 253	93.57 267	94.15 333	88.12 251	97.34 154	97.06 219	88.99 250	88.32 305	94.26 307	83.08 192	98.01 274	87.62 266	83.92 358	94.57 344

mvs_anonymous		93.82 147	93.74 130	94.06 236	96.44 219	85.41 309	95.81 276	97.05 220	89.85 223	90.09 256	96.36 197	87.44 126	97.75 310	93.97 132	96.69 173	99.02 90

IterMVS-LS		92.29 206	91.94 195	93.34 275	96.25 228	86.97 278	96.57 230	97.05 220	90.67 195	89.50 275	94.80 274	86.59 135	97.64 318	89.91 211	86.11 325	95.40 292
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		90.85 273	90.03 276	93.29 277	93.55 350	86.96 279	96.74 207	97.04 222	87.36 304	89.52 274	94.34 299	80.23 249	97.97 280	86.27 287	85.21 336	94.94 319

CDS-MVSNet		94.14 134	93.54 137	95.93 137	96.18 232	91.46 132	96.33 246	97.04 222	88.97 252	93.56 167	96.51 189	87.55 120	97.89 296	89.80 214	95.95 184	98.44 153
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v1144		91.37 248	90.60 249	93.68 262	93.89 341	88.23 246	96.84 199	97.03 224	88.37 274	89.69 267	94.39 295	82.04 217	97.98 277	87.80 256	85.37 332	94.84 327

v1240		90.70 279	89.85 282	93.23 279	93.51 353	86.80 280	96.61 224	97.02 225	87.16 309	89.58 270	94.31 303	79.55 262	97.98 277	85.52 302	85.44 331	94.90 324

EPP-MVSNet		95.22 101	95.04 100	95.76 144	97.49 148	89.56 199	98.67 10	97.00 226	90.69 193	94.24 153	97.62 124	89.79 87	98.81 187	93.39 147	96.49 177	98.92 104

V42		91.58 235	90.87 233	93.73 257	94.05 337	88.50 238	97.32 157	96.97 227	88.80 262	89.71 265	94.33 300	82.54 207	98.05 268	89.01 237	85.07 339	94.64 343

test_fmvs1		93.21 166	93.53 138	92.25 311	96.55 206	81.20 364	97.40 148	96.96 228	90.68 194	96.80 70	98.04 85	69.25 357	98.40 227	97.58 30	98.50 112	97.16 228

FMVSNet2		91.31 252	90.08 271	94.99 185	96.51 212	92.21 102	97.41 144	96.95 229	88.82 259	88.62 297	94.75 276	73.87 324	97.42 339	85.20 307	88.55 302	95.35 296

ACMH	87.59 16	90.53 284	89.42 297	93.87 251	96.21 229	87.92 255	97.24 163	96.94 230	88.45 272	83.91 369	96.27 201	71.92 336	98.62 211	84.43 315	89.43 293	95.05 315
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		91.35 249	90.27 262	94.59 207	96.51 212	91.18 146	97.50 133	96.93 231	88.82 259	89.35 278	94.51 288	73.87 324	97.29 346	86.12 292	88.82 297	95.31 299

test1		91.35 249	90.27 262	94.59 207	96.51 212	91.18 146	97.50 133	96.93 231	88.82 259	89.35 278	94.51 288	73.87 324	97.29 346	86.12 292	88.82 297	95.31 299

FMVSNet3		91.78 224	90.69 247	95.03 183	96.53 209	92.27 100	97.02 182	96.93 231	89.79 226	89.35 278	94.65 281	77.01 298	97.47 334	86.12 292	88.82 297	95.35 296

FMVSNet1		89.88 303	88.31 315	94.59 207	95.41 268	91.18 146	97.50 133	96.93 231	86.62 317	87.41 325	94.51 288	65.94 383	97.29 346	83.04 329	87.43 312	95.31 299

GeoE		93.89 144	93.28 150	95.72 150	96.96 175	89.75 194	98.24 38	96.92 235	89.47 234	92.12 203	97.21 148	84.42 166	98.39 231	87.71 259	96.50 176	99.01 93

miper_enhance_ethall		91.54 239	91.01 230	93.15 282	95.35 274	87.07 276	93.97 348	96.90 236	86.79 315	89.17 285	93.43 345	86.55 137	97.64 318	89.97 210	86.93 317	94.74 339

eth_miper_zixun_eth		91.02 266	90.59 250	92.34 308	95.33 278	84.35 328	94.10 345	96.90 236	88.56 268	88.84 293	94.33 300	84.08 173	97.60 323	88.77 243	84.37 352	95.06 314

TAMVS		94.01 140	93.46 143	95.64 153	96.16 234	90.45 172	96.71 211	96.89 238	89.27 241	93.46 172	96.92 163	87.29 129	97.94 289	88.70 244	95.74 189	98.53 139

miper_ehance_all_eth		91.59 233	91.13 226	92.97 288	95.55 261	86.57 288	94.47 329	96.88 239	87.77 293	88.88 291	94.01 318	86.22 142	97.54 327	89.49 222	86.93 317	94.79 335

v2v482		91.59 233	90.85 236	93.80 254	93.87 342	88.17 249	96.94 191	96.88 239	89.54 231	89.53 273	94.90 268	81.70 225	98.02 273	89.25 231	85.04 341	95.20 307

CNLPA		94.28 126	93.53 138	96.52 89	98.38 81	92.55 90	96.59 227	96.88 239	90.13 216	91.91 208	97.24 146	85.21 155	99.09 155	87.64 265	97.83 139	97.92 187

PAPM		91.52 240	90.30 260	95.20 174	95.30 281	89.83 192	93.38 368	96.85 242	86.26 325	88.59 298	95.80 225	84.88 159	98.15 250	75.67 381	95.93 185	97.63 204

c3_l		91.38 246	90.89 232	92.88 292	95.58 259	86.30 295	94.68 322	96.84 243	88.17 279	88.83 294	94.23 308	85.65 151	97.47 334	89.36 226	84.63 345	94.89 325

pm-mvs1		90.72 278	89.65 292	93.96 244	94.29 332	89.63 195	97.79 94	96.82 244	89.07 246	86.12 347	95.48 246	78.61 280	97.78 306	86.97 280	81.67 371	94.46 346

test_vis1_n		92.37 201	92.26 186	92.72 298	94.75 312	82.64 347	98.02 59	96.80 245	91.18 177	97.77 44	97.93 94	58.02 399	98.29 239	97.63 28	98.21 126	97.23 227

CMPMVS	62.92 21	85.62 353	84.92 349	87.74 375	89.14 397	73.12 405	94.17 343	96.80 245	73.98 404	73.65 403	94.93 266	66.36 377	97.61 322	83.95 322	91.28 270	92.48 381
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MS-PatchMatch		90.27 291	89.77 286	91.78 326	94.33 329	84.72 325	95.55 290	96.73 247	86.17 327	86.36 344	95.28 252	71.28 341	97.80 304	84.09 319	98.14 130	92.81 373

Effi-MVS+-dtu		93.08 173	93.21 152	92.68 301	96.02 243	83.25 342	97.14 175	96.72 248	93.85 82	91.20 233	93.44 342	83.08 192	98.30 238	91.69 181	95.73 190	96.50 245

TSAR-MVS + GP.		96.69 54	96.49 57	97.27 61	98.31 84	93.39 62	96.79 203	96.72 248	94.17 72	97.44 50	97.66 118	92.76 31	99.33 121	96.86 47	97.76 143	99.08 87

1112_ss		93.37 161	92.42 182	96.21 120	97.05 168	90.99 152	96.31 248	96.72 248	86.87 314	89.83 263	96.69 175	86.51 138	99.14 148	88.12 249	93.67 234	98.50 143

PVSNet	86.66 18	92.24 209	91.74 203	93.73 257	97.77 128	83.69 339	92.88 377	96.72 248	87.91 286	93.00 182	94.86 270	78.51 281	99.05 165	86.53 283	97.45 151	98.47 148

miper_lstm_enhance		90.50 287	90.06 275	91.83 322	95.33 278	83.74 336	93.86 354	96.70 252	87.56 300	87.79 317	93.81 326	83.45 184	96.92 358	87.39 270	84.62 346	94.82 330

v148		90.99 267	90.38 256	92.81 295	93.83 343	85.80 303	96.78 205	96.68 253	89.45 236	88.75 296	93.93 322	82.96 198	97.82 302	87.83 255	83.25 363	94.80 333

ACMH+	87.92 14	90.20 295	89.18 302	93.25 278	96.48 215	86.45 292	96.99 187	96.68 253	88.83 258	84.79 358	96.22 203	70.16 350	98.53 218	84.42 316	88.04 305	94.77 338

CANet_DTU		94.37 124	93.65 133	96.55 87	96.46 218	92.13 106	96.21 256	96.67 255	94.38 69	93.53 170	97.03 158	79.34 264	99.71 52	90.76 197	98.45 117	97.82 197

cl____		90.96 270	90.32 258	92.89 291	95.37 272	86.21 298	94.46 331	96.64 256	87.82 289	88.15 312	94.18 311	82.98 196	97.54 327	87.70 260	85.59 328	94.92 323

HY-MVS	89.66 9	93.87 145	92.95 157	96.63 82	97.10 162	92.49 92	95.64 288	96.64 256	89.05 248	93.00 182	95.79 228	85.77 150	99.45 111	89.16 236	94.35 216	97.96 185

Test_1112_low_res		92.84 187	91.84 198	95.85 142	97.04 169	89.97 188	95.53 292	96.64 256	85.38 337	89.65 269	95.18 257	85.86 148	99.10 152	87.70 260	93.58 239	98.49 145

DIV-MVS_self_test		90.97 269	90.33 257	92.88 292	95.36 273	86.19 299	94.46 331	96.63 259	87.82 289	88.18 311	94.23 308	82.99 195	97.53 329	87.72 257	85.57 329	94.93 321

Fast-Effi-MVS+-dtu		92.29 206	91.99 193	93.21 281	95.27 282	85.52 307	97.03 180	96.63 259	92.09 147	89.11 287	95.14 259	80.33 247	98.08 261	87.54 268	94.74 212	96.03 262

UnsupCasMVSNet_bld		82.13 367	79.46 372	90.14 358	88.00 405	82.47 352	90.89 395	96.62 261	78.94 394	75.61 398	84.40 409	56.63 402	96.31 368	77.30 373	66.77 410	91.63 391

cl22		91.21 257	90.56 252	93.14 283	96.09 241	86.80 280	94.41 333	96.58 262	87.80 291	88.58 299	93.99 320	80.85 238	97.62 321	89.87 213	86.93 317	94.99 316

jason		94.84 114	94.39 120	96.18 122	95.52 262	90.93 156	96.09 261	96.52 263	89.28 240	96.01 110	97.32 140	84.70 161	98.77 193	95.15 106	98.91 98	98.85 116
jason: jason.
tt0805		91.09 262	90.07 274	94.16 232	95.61 257	88.31 241	97.56 125	96.51 264	89.56 230	89.17 285	95.64 237	67.08 375	98.38 232	91.07 193	88.44 303	95.80 270

AUN-MVS		91.76 225	90.75 242	94.81 197	97.00 173	88.57 234	96.65 218	96.49 265	89.63 228	92.15 201	96.12 209	78.66 279	98.50 220	90.83 195	79.18 382	97.36 219

hse-mvs2		93.45 159	92.99 155	94.81 197	97.02 171	88.59 233	96.69 214	96.47 266	95.19 26	96.74 74	96.16 207	83.67 179	98.48 223	95.85 83	79.13 383	97.35 221

EG-PatchMatch MVS		87.02 337	85.44 341	91.76 328	92.67 371	85.00 319	96.08 262	96.45 267	83.41 367	79.52 390	93.49 339	57.10 401	97.72 312	79.34 364	90.87 279	92.56 378

KD-MVS_self_test		85.95 349	84.95 348	88.96 370	89.55 396	79.11 389	95.13 312	96.42 268	85.91 330	84.07 367	90.48 381	70.03 352	94.82 389	80.04 356	72.94 399	92.94 371

pmmvs6		87.81 329	86.19 336	92.69 300	91.32 384	86.30 295	97.34 154	96.41 269	80.59 388	84.05 368	94.37 297	67.37 370	97.67 315	84.75 311	79.51 381	94.09 358

PMMVS		92.86 185	92.34 183	94.42 219	94.92 303	86.73 283	94.53 327	96.38 270	84.78 349	94.27 152	95.12 261	83.13 191	98.40 227	91.47 185	96.49 177	98.12 175

RPSCF		90.75 276	90.86 234	90.42 354	96.84 180	76.29 397	95.61 289	96.34 271	83.89 358	91.38 221	97.87 99	76.45 303	98.78 190	87.16 277	92.23 252	96.20 252

BP-MVS1		95.89 82	95.49 82	97.08 72	96.67 195	93.20 72	98.08 53	96.32 272	94.56 57	96.32 95	97.84 104	84.07 174	99.15 145	96.75 49	98.78 101	98.90 108

MSDG		91.42 244	90.24 264	94.96 190	97.15 160	88.91 226	93.69 360	96.32 272	85.72 333	86.93 339	96.47 191	80.24 248	98.98 171	80.57 353	95.05 205	96.98 231

WBMVS		90.69 281	89.99 277	92.81 295	96.48 215	85.00 319	95.21 310	96.30 274	89.46 235	89.04 288	94.05 317	72.45 334	97.82 302	89.46 223	87.41 314	95.61 281

OurMVSNet-221017-0		90.51 286	90.19 269	91.44 334	93.41 357	81.25 362	96.98 188	96.28 275	91.68 158	86.55 343	96.30 199	74.20 323	97.98 277	88.96 239	87.40 315	95.09 312

MVP-Stereo		90.74 277	90.08 271	92.71 299	93.19 362	88.20 247	95.86 273	96.27 276	86.07 328	84.86 357	94.76 275	77.84 293	97.75 310	83.88 324	98.01 134	92.17 388
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
lupinMVS		94.99 109	94.56 111	96.29 114	96.34 225	91.21 141	95.83 275	96.27 276	88.93 254	96.22 100	96.88 165	86.20 144	98.85 183	95.27 102	99.05 90	98.82 120

BH-untuned		92.94 181	92.62 172	93.92 250	97.22 154	86.16 300	96.40 240	96.25 278	90.06 217	89.79 264	96.17 206	83.19 188	98.35 234	87.19 275	97.27 159	97.24 226

CL-MVSNet_self_test		86.31 344	85.15 345	89.80 362	88.83 400	81.74 360	93.93 351	96.22 279	86.67 316	85.03 355	90.80 379	78.09 289	94.50 390	74.92 384	71.86 401	93.15 369

IS-MVSNet		94.90 111	94.52 115	96.05 128	97.67 132	90.56 168	98.44 21	96.22 279	93.21 106	93.99 159	97.74 112	85.55 152	98.45 224	89.98 209	97.86 138	99.14 79

FA-MVS(test-final)		93.52 157	92.92 158	95.31 171	96.77 190	88.54 236	94.82 319	96.21 281	89.61 229	94.20 154	95.25 255	83.24 186	99.14 148	90.01 208	96.16 181	98.25 164

GA-MVS		91.38 246	90.31 259	94.59 207	94.65 317	87.62 263	94.34 336	96.19 282	90.73 191	90.35 244	93.83 323	71.84 337	97.96 284	87.22 274	93.61 237	98.21 167

IterMVS-SCA-FT		90.31 289	89.81 284	91.82 323	95.52 262	84.20 331	94.30 339	96.15 283	90.61 201	87.39 326	94.27 305	75.80 309	96.44 366	87.34 271	86.88 321	94.82 330

IterMVS		90.15 297	89.67 290	91.61 330	95.48 264	83.72 337	94.33 337	96.12 284	89.99 218	87.31 329	94.15 313	75.78 311	96.27 369	86.97 280	86.89 320	94.83 328
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		92.76 190	91.51 212	96.52 89	98.77 56	90.99 152	97.38 151	96.08 285	82.38 373	89.29 281	97.87 99	83.77 177	99.69 58	81.37 347	96.69 173	98.89 112

pmmvs4		90.93 271	89.85 282	94.17 231	93.34 359	90.79 161	94.60 324	96.02 286	84.62 350	87.45 323	95.15 258	81.88 222	97.45 336	87.70 260	87.87 307	94.27 355

ppachtmachnet_test		88.35 324	87.29 323	91.53 331	92.45 377	83.57 340	93.75 357	95.97 287	84.28 353	85.32 354	94.18 311	79.00 276	96.93 357	75.71 380	84.99 342	94.10 356

Anonymous20240521		86.42 342	85.44 341	89.34 368	90.33 389	79.79 381	96.73 208	95.92 288	83.71 363	83.25 373	91.36 376	63.92 388	96.01 370	78.39 368	85.36 333	92.22 386

ITE_SJBPF				92.43 304	95.34 275	85.37 312		95.92 288	91.47 163	87.75 319	96.39 196	71.00 343	97.96 284	82.36 338	89.86 289	93.97 359

test_fmvs2		89.77 307	89.93 279	89.31 369	93.68 348	76.37 396	97.64 116	95.90 290	89.84 224	91.49 219	96.26 202	58.77 398	97.10 350	94.65 121	91.13 272	94.46 346

USDC		88.94 315	87.83 320	92.27 310	94.66 316	84.96 321	93.86 354	95.90 290	87.34 305	83.40 371	95.56 241	67.43 369	98.19 247	82.64 337	89.67 291	93.66 362

COLMAP_ROB	87.81 15	90.40 288	89.28 300	93.79 255	97.95 117	87.13 275	96.92 192	95.89 292	82.83 370	86.88 341	97.18 149	73.77 327	99.29 128	78.44 367	93.62 236	94.95 317
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VDD-MVS		93.82 147	93.08 153	96.02 131	97.88 123	89.96 189	97.72 103	95.85 293	92.43 136	95.86 114	98.44 49	68.42 365	99.39 117	96.31 60	94.85 206	98.71 127

VDDNet		93.05 175	92.07 189	96.02 131	96.84 180	90.39 176	98.08 53	95.85 293	86.22 326	95.79 117	98.46 47	67.59 368	99.19 136	94.92 111	94.85 206	98.47 148

mvsmamba		94.57 120	94.14 124	95.87 139	97.03 170	89.93 190	97.84 85	95.85 293	91.34 169	94.79 140	96.80 167	80.67 239	98.81 187	94.85 112	98.12 131	98.85 116

Vis-MVSNet (Re-imp)		94.15 131	93.88 128	94.95 191	97.61 140	87.92 255	98.10 51	95.80 296	92.22 140	93.02 181	97.45 133	84.53 164	97.91 295	88.24 248	97.97 135	99.02 90

MM		97.29 22	96.98 30	98.23 11	98.01 111	95.03 26	98.07 55	95.76 297	97.78 1	97.52 47	98.80 28	88.09 107	99.86 9	99.44 1	99.37 61	99.80 1

KD-MVS_2432*1600		84.81 357	82.64 361	91.31 336	91.07 386	85.34 313	91.22 390	95.75 298	85.56 335	83.09 374	90.21 384	67.21 371	95.89 372	77.18 374	62.48 414	92.69 374

miper_refine_blended		84.81 357	82.64 361	91.31 336	91.07 386	85.34 313	91.22 390	95.75 298	85.56 335	83.09 374	90.21 384	67.21 371	95.89 372	77.18 374	62.48 414	92.69 374

FE-MVS		92.05 216	91.05 228	95.08 180	96.83 182	87.93 254	93.91 353	95.70 300	86.30 323	94.15 156	94.97 263	76.59 301	99.21 134	84.10 318	96.86 166	98.09 179

tpm cat1		88.36 323	87.21 326	91.81 324	95.13 293	80.55 371	92.58 381	95.70 300	74.97 403	87.45 323	91.96 370	78.01 292	98.17 249	80.39 355	88.74 300	96.72 241

our_test_3		88.78 319	87.98 319	91.20 340	92.45 377	82.53 349	93.61 364	95.69 302	85.77 332	84.88 356	93.71 328	79.99 253	96.78 363	79.47 361	86.24 322	94.28 354

BH-w/o		92.14 214	91.75 201	93.31 276	96.99 174	85.73 304	95.67 283	95.69 302	88.73 264	89.26 283	94.82 273	82.97 197	98.07 265	85.26 306	96.32 180	96.13 258

CR-MVSNet		90.82 274	89.77 286	93.95 245	94.45 325	87.19 272	90.23 398	95.68 304	86.89 313	92.40 191	92.36 363	80.91 235	97.05 352	81.09 351	93.95 230	97.60 209

Patchmtry		88.64 321	87.25 324	92.78 297	94.09 335	86.64 284	89.82 402	95.68 304	80.81 385	87.63 321	92.36 363	80.91 235	97.03 353	78.86 365	85.12 338	94.67 341

testing91		91.90 221	91.02 229	94.53 214	96.54 207	86.55 290	95.86 273	95.64 306	91.77 155	91.89 209	93.47 341	69.94 353	98.86 181	90.23 207	93.86 232	98.18 169

BH-RMVSNet		92.72 192	91.97 194	94.97 189	97.16 158	87.99 253	96.15 259	95.60 307	90.62 200	91.87 210	97.15 152	78.41 283	98.57 216	83.16 327	97.60 145	98.36 160

PVSNet_0	82.17 19	85.46 354	83.64 357	90.92 343	95.27 282	79.49 385	90.55 396	95.60 307	83.76 362	83.00 376	89.95 386	71.09 342	97.97 280	82.75 335	60.79 416	95.31 299

SCA		91.84 223	91.18 225	93.83 252	95.59 258	84.95 322	94.72 321	95.58 309	90.82 187	92.25 199	93.69 330	75.80 309	98.10 256	86.20 289	95.98 183	98.45 150

MonoMVSNet		91.92 219	91.77 199	92.37 305	92.94 366	83.11 343	97.09 178	95.55 310	92.91 126	90.85 236	94.55 285	81.27 231	96.52 365	93.01 157	87.76 308	97.47 215

AllTest		90.23 293	88.98 305	93.98 241	97.94 118	86.64 284	96.51 231	95.54 311	85.38 337	85.49 351	96.77 169	70.28 348	99.15 145	80.02 357	92.87 241	96.15 256

TestCases				93.98 241	97.94 118	86.64 284		95.54 311	85.38 337	85.49 351	96.77 169	70.28 348	99.15 145	80.02 357	92.87 241	96.15 256

mmtdpeth		89.70 308	88.96 306	91.90 319	95.84 251	84.42 327	97.46 142	95.53 313	90.27 211	94.46 149	90.50 380	69.74 356	98.95 172	97.39 39	69.48 405	92.34 382

tpmvs		89.83 306	89.15 303	91.89 320	94.92 303	80.30 375	93.11 373	95.46 314	86.28 324	88.08 313	92.65 353	80.44 244	98.52 219	81.47 343	89.92 288	96.84 237

pmmvs5		89.86 305	88.87 309	92.82 294	92.86 367	86.23 297	96.26 251	95.39 315	84.24 354	87.12 330	94.51 288	74.27 322	97.36 343	87.61 267	87.57 310	94.86 326

PatchmatchNet		91.91 220	91.35 214	93.59 265	95.38 270	84.11 332	93.15 372	95.39 315	89.54 231	92.10 204	93.68 332	82.82 201	98.13 251	84.81 310	95.32 198	98.52 140
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		91.44 243	91.32 216	91.79 325	95.15 291	79.20 388	93.42 367	95.37 317	88.55 269	93.49 171	93.67 333	82.49 209	98.27 240	90.41 202	89.34 294	97.90 188

Anonymous20231206		87.09 336	86.14 337	89.93 361	91.22 385	80.35 373	96.11 260	95.35 318	83.57 365	84.16 363	93.02 348	73.54 329	95.61 380	72.16 396	86.14 324	93.84 361

MIMVSNet1		84.93 356	83.05 358	90.56 352	89.56 395	84.84 324	95.40 297	95.35 318	83.91 357	80.38 386	92.21 367	57.23 400	93.34 402	70.69 402	82.75 369	93.50 364

TDRefinement		86.53 339	84.76 351	91.85 321	82.23 417	84.25 329	96.38 242	95.35 318	84.97 346	84.09 366	94.94 265	65.76 384	98.34 237	84.60 314	74.52 395	92.97 370

TR-MVS		91.48 242	90.59 250	94.16 232	96.40 221	87.33 265	95.67 283	95.34 321	87.68 297	91.46 220	95.52 244	76.77 300	98.35 234	82.85 332	93.61 237	96.79 239

EPNet_dtu		91.71 226	91.28 219	92.99 287	93.76 345	83.71 338	96.69 214	95.28 322	93.15 113	87.02 335	95.95 217	83.37 185	97.38 342	79.46 362	96.84 167	97.88 190
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet5		87.29 333	85.79 339	91.78 326	94.80 310	87.28 267	95.49 294	95.28 322	84.09 356	83.85 370	91.82 371	62.95 391	94.17 394	78.48 366	85.34 334	93.91 360

MDTV_nov1_ep13			90.76 240		95.22 286	80.33 374	93.03 375	95.28 322	88.14 281	92.84 188	93.83 323	81.34 228	98.08 261	82.86 330	94.34 217

LF4IMVS		87.94 327	87.25 324	89.98 360	92.38 379	80.05 380	94.38 334	95.25 325	87.59 299	84.34 360	94.74 277	64.31 387	97.66 317	84.83 309	87.45 311	92.23 385

TransMVSNet (Re)		88.94 315	87.56 321	93.08 285	94.35 328	88.45 240	97.73 100	95.23 326	87.47 301	84.26 362	95.29 250	79.86 256	97.33 344	79.44 363	74.44 396	93.45 366

test20.03		86.14 347	85.40 343	88.35 371	90.12 390	80.06 379	95.90 272	95.20 327	88.59 265	81.29 381	93.62 335	71.43 340	92.65 405	71.26 400	81.17 374	92.34 382

new-patchmatchnet		83.18 363	81.87 366	87.11 378	86.88 408	75.99 398	93.70 358	95.18 328	85.02 345	77.30 397	88.40 396	65.99 382	93.88 399	74.19 389	70.18 403	91.47 395

MDA-MVSNet_test_wron		85.87 351	84.23 355	90.80 349	92.38 379	82.57 348	93.17 370	95.15 329	82.15 374	67.65 409	92.33 366	78.20 285	95.51 383	77.33 371	79.74 378	94.31 353

YYNet1		85.87 351	84.23 355	90.78 350	92.38 379	82.46 353	93.17 370	95.14 330	82.12 375	67.69 407	92.36 363	78.16 288	95.50 384	77.31 372	79.73 379	94.39 349

Baseline_NR-MVSNet		91.20 258	90.62 248	92.95 289	93.83 343	88.03 252	97.01 185	95.12 331	88.42 273	89.70 266	95.13 260	83.47 182	97.44 337	89.66 219	83.24 364	93.37 367

thres200		92.23 210	91.39 213	94.75 204	97.61 140	89.03 224	96.60 226	95.09 332	92.08 148	93.28 177	94.00 319	78.39 284	99.04 168	81.26 350	94.18 221	96.19 253

ADS-MVSNet		89.89 302	88.68 311	93.53 268	95.86 246	84.89 323	90.93 393	95.07 333	83.23 368	91.28 229	91.81 372	79.01 274	97.85 298	79.52 359	91.39 268	97.84 194

pmmvs-eth3d		86.22 345	84.45 353	91.53 331	88.34 404	87.25 269	94.47 329	95.01 334	83.47 366	79.51 391	89.61 389	69.75 355	95.71 377	83.13 328	76.73 390	91.64 390

Anonymous202405211		92.07 215	90.83 238	95.76 144	98.19 98	88.75 229	97.58 122	95.00 335	86.00 329	93.64 166	97.45 133	66.24 380	99.53 97	90.68 200	92.71 246	99.01 93

MDA-MVSNet-bldmvs		85.00 355	82.95 360	91.17 341	93.13 364	83.33 341	94.56 326	95.00 335	84.57 351	65.13 413	92.65 353	70.45 347	95.85 374	73.57 392	77.49 386	94.33 351

ambc				86.56 381	83.60 414	70.00 408	85.69 412	94.97 337		80.60 385	88.45 395	37.42 416	96.84 361	82.69 336	75.44 394	92.86 372

testgi		87.97 326	87.21 326	90.24 357	92.86 367	80.76 366	96.67 217	94.97 337	91.74 156	85.52 350	95.83 223	62.66 393	94.47 392	76.25 378	88.36 304	95.48 284

dp		88.90 317	88.26 317	90.81 347	94.58 321	76.62 395	92.85 378	94.93 339	85.12 343	90.07 258	93.07 347	75.81 308	98.12 254	80.53 354	87.42 313	97.71 201

test_fmvs3		83.21 362	83.02 359	83.78 385	86.77 409	68.34 411	96.76 206	94.91 340	86.49 319	84.14 365	89.48 390	36.04 417	91.73 407	91.86 175	80.77 376	91.26 397

test_0402		86.46 341	84.79 350	91.45 333	95.02 297	85.55 306	96.29 250	94.89 341	80.90 382	82.21 378	93.97 321	68.21 366	97.29 346	62.98 408	88.68 301	91.51 393

tfpn200view9		92.38 200	91.52 210	94.95 191	97.85 124	89.29 214	97.41 144	94.88 342	92.19 144	93.27 178	94.46 293	78.17 286	99.08 158	81.40 344	94.08 225	96.48 246

CVMVSNet		91.23 256	91.75 201	89.67 363	95.77 252	74.69 399	96.44 232	94.88 342	85.81 331	92.18 200	97.64 122	79.07 269	95.58 382	88.06 251	95.86 187	98.74 124

thres400		92.42 198	91.52 210	95.12 179	97.85 124	89.29 214	97.41 144	94.88 342	92.19 144	93.27 178	94.46 293	78.17 286	99.08 158	81.40 344	94.08 225	96.98 231

EPNet		95.20 102	94.56 111	97.14 68	92.80 369	92.68 86	97.85 84	94.87 345	96.64 3	92.46 190	97.80 109	86.23 141	99.65 64	93.72 140	98.62 108	99.10 85
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing99		91.62 231	90.72 245	94.32 224	96.48 215	86.11 301	95.81 276	94.76 346	91.55 160	91.75 214	93.44 342	68.55 363	98.82 185	90.43 201	93.69 233	98.04 182

SixPastTwentyTwo		89.15 313	88.54 313	90.98 342	93.49 354	80.28 376	96.70 212	94.70 347	90.78 188	84.15 364	95.57 240	71.78 338	97.71 313	84.63 313	85.07 339	94.94 319

thres100view900		92.43 197	91.58 207	94.98 187	97.92 120	89.37 210	97.71 105	94.66 348	92.20 142	93.31 176	94.90 268	78.06 290	99.08 158	81.40 344	94.08 225	96.48 246

thres600view7		92.49 196	91.60 206	95.18 175	97.91 121	89.47 204	97.65 112	94.66 348	92.18 146	93.33 175	94.91 267	78.06 290	99.10 152	81.61 341	94.06 229	96.98 231

PatchT		88.87 318	87.42 322	93.22 280	94.08 336	85.10 317	89.51 403	94.64 350	81.92 376	92.36 194	88.15 399	80.05 252	97.01 355	72.43 395	93.65 235	97.54 212

baseline1		92.82 188	91.90 196	95.55 160	97.20 156	90.77 162	97.19 170	94.58 351	92.20 142	92.36 194	96.34 198	84.16 172	98.21 244	89.20 234	83.90 359	97.68 203

UBG		91.55 237	90.76 240	93.94 247	96.52 211	85.06 318	95.22 308	94.54 352	90.47 207	91.98 207	92.71 352	72.02 335	98.74 197	88.10 250	95.26 200	98.01 183

Gipuma		67.86 383	65.41 385	75.18 398	92.66 372	73.45 402	66.50 419	94.52 353	53.33 418	57.80 419	66.07 419	30.81 419	89.20 411	48.15 417	78.88 385	62.90 419
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testing11		91.68 229	90.75 242	94.47 215	96.53 209	86.56 289	95.76 280	94.51 354	91.10 182	91.24 231	93.59 336	68.59 362	98.86 181	91.10 192	94.29 218	98.00 184

CostFormer		91.18 261	90.70 246	92.62 302	94.84 308	81.76 359	94.09 346	94.43 355	84.15 355	92.72 189	93.77 327	79.43 263	98.20 245	90.70 199	92.18 255	97.90 188

tpm2		89.96 299	89.21 301	92.23 312	94.91 305	81.25 362	93.78 356	94.42 356	80.62 387	91.56 217	93.44 342	76.44 304	97.94 289	85.60 301	92.08 259	97.49 213

MVS_0304		96.74 51	96.31 67	98.02 19	96.87 177	94.65 30	97.58 122	94.39 357	96.47 6	97.16 59	98.39 53	87.53 122	99.87 7	98.97 11	99.41 53	99.55 34

JIA-IIPM		88.26 325	87.04 329	91.91 318	93.52 352	81.42 361	89.38 404	94.38 358	80.84 384	90.93 235	80.74 411	79.22 266	97.92 292	82.76 334	91.62 263	96.38 249

dmvs_re		90.21 294	89.50 295	92.35 306	95.47 267	85.15 315	95.70 282	94.37 359	90.94 186	88.42 301	93.57 337	74.63 319	95.67 379	82.80 333	89.57 292	96.22 251

Patchmatch-test		89.42 311	87.99 318	93.70 260	95.27 282	85.11 316	88.98 405	94.37 359	81.11 381	87.10 333	93.69 330	82.28 213	97.50 332	74.37 387	94.76 210	98.48 147

LCM-MVSNet		72.55 376	69.39 380	82.03 387	70.81 427	65.42 416	90.12 400	94.36 361	55.02 417	65.88 411	81.72 410	24.16 425	89.96 408	74.32 388	68.10 408	90.71 400

ADS-MVSNet2		89.45 310	88.59 312	92.03 315	95.86 246	82.26 355	90.93 393	94.32 362	83.23 368	91.28 229	91.81 372	79.01 274	95.99 371	79.52 359	91.39 268	97.84 194

mvs5depth		86.53 339	85.08 346	90.87 344	88.74 402	82.52 350	91.91 386	94.23 363	86.35 322	87.11 332	93.70 329	66.52 376	97.76 309	81.37 347	75.80 392	92.31 384

EU-MVSNet		88.72 320	88.90 308	88.20 373	93.15 363	74.21 400	96.63 223	94.22 364	85.18 341	87.32 328	95.97 215	76.16 306	94.98 388	85.27 305	86.17 323	95.41 289

MIMVSNet		88.50 322	86.76 332	93.72 259	94.84 308	87.77 261	91.39 388	94.05 365	86.41 321	87.99 315	92.59 356	63.27 389	95.82 376	77.44 370	92.84 243	97.57 211

OpenMVS_ROB	81.14 20	84.42 359	82.28 365	90.83 345	90.06 391	84.05 334	95.73 281	94.04 366	73.89 406	80.17 389	91.53 375	59.15 397	97.64 318	66.92 406	89.05 296	90.80 399

TinyColmap		86.82 338	85.35 344	91.21 338	94.91 305	82.99 345	93.94 350	94.02 367	83.58 364	81.56 380	94.68 279	62.34 394	98.13 251	75.78 379	87.35 316	92.52 380

ETVMVS		90.52 285	89.14 304	94.67 206	96.81 186	87.85 259	95.91 271	93.97 368	89.71 227	92.34 197	92.48 358	65.41 385	97.96 284	81.37 347	94.27 219	98.21 167

IB-MVS	87.33 17	89.91 300	88.28 316	94.79 201	95.26 285	87.70 262	95.12 313	93.95 369	89.35 239	87.03 334	92.49 357	70.74 345	99.19 136	89.18 235	81.37 373	97.49 213
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		87.13 335	87.02 330	87.47 376	95.16 289	73.21 404	95.00 315	93.93 370	88.55 269	86.96 336	91.99 368	75.90 307	94.00 396	61.59 410	94.11 222	95.20 307

myMVS_eth3d		87.18 334	86.38 334	89.58 364	95.16 289	79.53 383	95.00 315	93.93 370	88.55 269	86.96 336	91.99 368	56.23 403	94.00 396	75.47 383	94.11 222	95.20 307

testing222		90.31 289	88.96 306	94.35 221	96.54 207	87.29 266	95.50 293	93.84 372	90.97 185	91.75 214	92.96 349	62.18 395	98.00 275	82.86 330	94.08 225	97.76 199

test_f		80.57 369	79.62 371	83.41 386	83.38 415	67.80 413	93.57 365	93.72 373	80.80 386	77.91 396	87.63 402	33.40 418	92.08 406	87.14 278	79.04 384	90.34 401

LCM-MVSNet-Re		92.50 194	92.52 178	92.44 303	96.82 184	81.89 358	96.92 192	93.71 374	92.41 137	84.30 361	94.60 283	85.08 157	97.03 353	91.51 183	97.36 153	98.40 156

tpm		90.25 292	89.74 289	91.76 328	93.92 339	79.73 382	93.98 347	93.54 375	88.28 276	91.99 206	93.25 346	77.51 296	97.44 337	87.30 273	87.94 306	98.12 175

ET-MVSNet_ETH3D		91.49 241	90.11 270	95.63 154	96.40 221	91.57 127	95.34 299	93.48 376	90.60 203	75.58 399	95.49 245	80.08 251	96.79 362	94.25 128	89.76 290	98.52 140

LFMVS		93.60 153	92.63 171	96.52 89	98.13 103	91.27 138	97.94 73	93.39 377	90.57 204	96.29 97	98.31 66	69.00 358	99.16 143	94.18 129	95.87 186	99.12 83

MVStest1		82.38 366	80.04 370	89.37 366	87.63 407	82.83 346	95.03 314	93.37 378	73.90 405	73.50 404	94.35 298	62.89 392	93.25 403	73.80 390	65.92 411	92.04 389

Patchmatch-RL test		87.38 332	86.24 335	90.81 347	88.74 402	78.40 392	88.12 410	93.17 379	87.11 310	82.17 379	89.29 391	81.95 220	95.60 381	88.64 245	77.02 387	98.41 155

ttmdpeth		85.91 350	84.76 351	89.36 367	89.14 397	80.25 377	95.66 286	93.16 380	83.77 361	83.39 372	95.26 254	66.24 380	95.26 387	80.65 352	75.57 393	92.57 377

test-LLR		91.42 244	91.19 224	92.12 313	94.59 319	80.66 368	94.29 340	92.98 381	91.11 180	90.76 238	92.37 360	79.02 272	98.07 265	88.81 241	96.74 170	97.63 204

test-mter		90.19 296	89.54 294	92.12 313	94.59 319	80.66 368	94.29 340	92.98 381	87.68 297	90.76 238	92.37 360	67.67 367	98.07 265	88.81 241	96.74 170	97.63 204

WB-MVSnew		89.88 303	89.56 293	90.82 346	94.57 322	83.06 344	95.65 287	92.85 383	87.86 288	90.83 237	94.10 314	79.66 260	96.88 359	76.34 377	94.19 220	92.54 379

testing3		87.67 330	86.88 331	90.05 359	96.14 237	80.71 367	97.10 177	92.85 383	90.15 215	87.54 322	94.55 285	55.70 404	94.10 395	73.77 391	94.10 224	95.35 296

test_method		66.11 384	64.89 386	69.79 401	72.62 425	35.23 433	65.19 420	92.83 385	20.35 423	65.20 412	88.08 400	43.14 414	82.70 418	73.12 394	63.46 413	91.45 396

test0.0.03 1		89.37 312	88.70 310	91.41 335	92.47 376	85.63 305	95.22 308	92.70 386	91.11 180	86.91 340	93.65 334	79.02 272	93.19 404	78.00 369	89.18 295	95.41 289

new_pmnet		82.89 364	81.12 369	88.18 374	89.63 394	80.18 378	91.77 387	92.57 387	76.79 401	75.56 400	88.23 398	61.22 396	94.48 391	71.43 398	82.92 367	89.87 402

mvsany_test1		93.93 143	93.98 126	93.78 256	94.94 302	86.80 280	94.62 323	92.55 388	88.77 263	96.85 69	98.49 43	88.98 93	98.08 261	95.03 108	95.62 193	96.46 248

thisisatest0515		92.29 206	91.30 218	95.25 173	96.60 199	88.90 227	94.36 335	92.32 389	87.92 285	93.43 173	94.57 284	77.28 297	99.00 169	89.42 225	95.86 187	97.86 193

thisisatest0530		93.03 176	92.21 187	95.49 164	97.07 163	89.11 223	97.49 139	92.19 390	90.16 214	94.09 157	96.41 194	76.43 305	99.05 165	90.38 203	95.68 192	98.31 162

tttt0517		92.96 179	92.33 184	94.87 194	97.11 161	87.16 274	97.97 69	92.09 391	90.63 199	93.88 163	97.01 159	76.50 302	99.06 164	90.29 206	95.45 196	98.38 158

K. test v3		87.64 331	86.75 333	90.32 356	93.02 365	79.48 386	96.61 224	92.08 392	90.66 197	80.25 388	94.09 315	67.21 371	96.65 364	85.96 297	80.83 375	94.83 328

TESTMET0.1,1		90.06 298	89.42 297	91.97 316	94.41 327	80.62 370	94.29 340	91.97 393	87.28 307	90.44 242	92.47 359	68.79 359	97.67 315	88.50 247	96.60 175	97.61 208

PM-MVS		83.48 361	81.86 367	88.31 372	87.83 406	77.59 394	93.43 366	91.75 394	86.91 312	80.63 384	89.91 387	44.42 413	95.84 375	85.17 308	76.73 390	91.50 394

baseline2		91.63 230	90.86 234	93.94 247	94.33 329	86.32 294	95.92 270	91.64 395	89.37 238	86.94 338	94.69 278	81.62 226	98.69 203	88.64 245	94.57 215	96.81 238

APD_test1		79.31 371	77.70 374	84.14 384	89.11 399	69.07 410	92.36 385	91.50 396	69.07 409	73.87 402	92.63 355	39.93 415	94.32 393	70.54 403	80.25 377	89.02 404

FPMVS		71.27 377	69.85 379	75.50 397	74.64 422	59.03 422	91.30 389	91.50 396	58.80 414	57.92 418	88.28 397	29.98 421	85.53 417	53.43 415	82.84 368	81.95 410

door								91.13 398

door-mid								91.06 399

EGC-MVSNET		68.77 382	63.01 388	86.07 383	92.49 375	82.24 356	93.96 349	90.96 400	0.71 428	2.62 429	90.89 378	53.66 405	93.46 400	57.25 413	84.55 349	82.51 409

mvsany_test3		83.59 360	82.44 364	87.03 379	83.80 412	73.82 401	93.70 358	90.92 401	86.42 320	82.51 377	90.26 383	46.76 412	95.71 377	90.82 196	76.76 389	91.57 392

pmmvs3		79.97 370	77.50 375	87.39 377	82.80 416	79.38 387	92.70 380	90.75 402	70.69 408	78.66 393	87.47 404	51.34 408	93.40 401	73.39 393	69.65 404	89.38 403

UWE-MVS		89.91 300	89.48 296	91.21 338	95.88 245	78.23 393	94.91 318	90.26 403	89.11 245	92.35 196	94.52 287	68.76 360	97.96 284	83.95 322	95.59 194	97.42 217

DSMNet-mixed		86.34 343	86.12 338	87.00 380	89.88 393	70.43 406	94.93 317	90.08 404	77.97 398	85.42 353	92.78 351	74.44 321	93.96 398	74.43 386	95.14 201	96.62 242

MVS-HIRNet		82.47 365	81.21 368	86.26 382	95.38 270	69.21 409	88.96 406	89.49 405	66.28 411	80.79 383	74.08 416	68.48 364	97.39 341	71.93 397	95.47 195	92.18 387

WB-MVS		76.77 373	76.63 376	77.18 392	85.32 410	56.82 424	94.53 327	89.39 406	82.66 372	71.35 405	89.18 392	75.03 316	88.88 412	35.42 421	66.79 409	85.84 406

test1111		93.19 168	92.82 162	94.30 227	97.58 146	84.56 326	98.21 42	89.02 407	93.53 95	94.58 144	98.21 73	72.69 331	99.05 165	93.06 153	98.48 115	99.28 68

SSC-MVS		76.05 374	75.83 377	76.72 396	84.77 411	56.22 425	94.32 338	88.96 408	81.82 378	70.52 406	88.91 393	74.79 318	88.71 413	33.69 422	64.71 412	85.23 407

ECVR-MVS		93.19 168	92.73 168	94.57 212	97.66 134	85.41 309	98.21 42	88.23 409	93.43 99	94.70 142	98.21 73	72.57 332	99.07 162	93.05 154	98.49 113	99.25 71

EPMVS		90.70 279	89.81 284	93.37 274	94.73 314	84.21 330	93.67 361	88.02 410	89.50 233	92.38 193	93.49 339	77.82 294	97.78 306	86.03 295	92.68 247	98.11 178

ANet_high		63.94 386	59.58 389	77.02 393	61.24 429	66.06 414	85.66 413	87.93 411	78.53 396	42.94 421	71.04 418	25.42 424	80.71 420	52.60 416	30.83 422	84.28 408

PMMVS2		70.19 378	66.92 382	80.01 388	76.35 421	65.67 415	86.22 411	87.58 412	64.83 413	62.38 414	80.29 413	26.78 423	88.49 415	63.79 407	54.07 418	85.88 405

lessismore_v0				90.45 353	91.96 382	79.09 390		87.19 413		80.32 387	94.39 295	66.31 379	97.55 326	84.00 321	76.84 388	94.70 340

PMVS	53.92 22	58.58 387	55.40 390	68.12 402	51.00 430	48.64 427	78.86 416	87.10 414	46.77 419	35.84 425	74.28 415	8.76 429	86.34 416	42.07 419	73.91 397	69.38 416
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_vis1_rt		86.16 346	85.06 347	89.46 365	93.47 356	80.46 372	96.41 236	86.61 415	85.22 340	79.15 392	88.64 394	52.41 407	97.06 351	93.08 152	90.57 281	90.87 398

testf1		69.31 380	66.76 383	76.94 394	78.61 419	61.93 418	88.27 408	86.11 416	55.62 415	59.69 415	85.31 407	20.19 427	89.32 409	57.62 411	69.44 406	79.58 411

APD_test2		69.31 380	66.76 383	76.94 394	78.61 419	61.93 418	88.27 408	86.11 416	55.62 415	59.69 415	85.31 407	20.19 427	89.32 409	57.62 411	69.44 406	79.58 411

gg-mvs-nofinetune		87.82 328	85.61 340	94.44 217	94.46 324	89.27 217	91.21 392	84.61 418	80.88 383	89.89 262	74.98 414	71.50 339	97.53 329	85.75 300	97.21 161	96.51 244

dmvs_testset		81.38 368	82.60 363	77.73 391	91.74 383	51.49 426	93.03 375	84.21 419	89.07 246	78.28 395	91.25 377	76.97 299	88.53 414	56.57 414	82.24 370	93.16 368

GG-mvs-BLEND				93.62 263	93.69 347	89.20 219	92.39 384	83.33 420		87.98 316	89.84 388	71.00 343	96.87 360	82.08 340	95.40 197	94.80 333

MTMP							97.86 81	82.03 421

DeepMVS_CX				74.68 399	90.84 388	64.34 417		81.61 422	65.34 412	67.47 410	88.01 401	48.60 411	80.13 421	62.33 409	73.68 398	79.58 411

E-PMN		53.28 388	52.56 392	55.43 405	74.43 423	47.13 428	83.63 415	76.30 423	42.23 420	42.59 422	62.22 421	28.57 422	74.40 422	31.53 423	31.51 421	44.78 420

test2506		91.60 232	90.78 239	94.04 238	97.66 134	83.81 335	98.27 32	75.53 424	93.43 99	95.23 131	98.21 73	67.21 371	99.07 162	93.01 157	98.49 113	99.25 71

EMVS		52.08 390	51.31 393	54.39 406	72.62 425	45.39 430	83.84 414	75.51 425	41.13 421	40.77 423	59.65 422	30.08 420	73.60 423	28.31 425	29.90 423	44.18 421

test_vis3_rt		72.73 375	70.55 378	79.27 389	80.02 418	68.13 412	93.92 352	74.30 426	76.90 400	58.99 417	73.58 417	20.29 426	95.37 385	84.16 317	72.80 400	74.31 414

MVE	50.73 23	53.25 389	48.81 394	66.58 404	65.34 428	57.50 423	72.49 418	70.94 427	40.15 422	39.28 424	63.51 420	6.89 431	73.48 424	38.29 420	42.38 420	68.76 418
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		51.94 391	53.82 391	46.29 407	33.73 431	45.30 431	78.32 417	67.24 428	18.02 424	50.93 420	87.05 405	52.99 406	53.11 426	70.76 401	25.29 424	40.46 422

kuosan		65.27 385	64.66 387	67.11 403	83.80 412	61.32 421	88.53 407	60.77 429	68.22 410	67.67 408	80.52 412	49.12 410	70.76 425	29.67 424	53.64 419	69.26 417

dongtai		69.99 379	69.33 381	71.98 400	88.78 401	61.64 420	89.86 401	59.93 430	75.67 402	74.96 401	85.45 406	50.19 409	81.66 419	43.86 418	55.27 417	72.63 415

N_pmnet		78.73 372	78.71 373	78.79 390	92.80 369	46.50 429	94.14 344	43.71 431	78.61 395	80.83 382	91.66 374	74.94 317	96.36 367	67.24 405	84.45 351	93.50 364

wuyk23d		25.11 392	24.57 396	26.74 408	73.98 424	39.89 432	57.88 421	9.80 432	12.27 425	10.39 426	6.97 428	7.03 430	36.44 427	25.43 426	17.39 425	3.89 425

testmvs		13.36 394	16.33 397	4.48 410	5.04 432	2.26 435	93.18 369	3.28 433	2.70 426	8.24 427	21.66 424	2.29 433	2.19 428	7.58 427	2.96 426	9.00 424

test123		13.04 395	15.66 398	5.18 409	4.51 433	3.45 434	92.50 383	1.81 434	2.50 427	7.58 428	20.15 425	3.67 432	2.18 429	7.13 428	1.07 427	9.90 423

mmdepth		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

monomultidepth		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

test_blank		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

uanet_test		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

DCPMVS		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

pcd_1.5k_mvsjas		7.39 397	9.85 400	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	88.65 100	0.00 430	0.00 429	0.00 428	0.00 426

sosnet-low-res		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

sosnet		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

uncertanet		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

Regformer		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

n2								0.00 435

nn								0.00 435

ab-mvs-re		8.06 396	10.74 399	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	96.69 175	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

uanet		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

WAC-MVS						79.53 383								75.56 382

PC_three_1452									90.77 189	98.89 17	98.28 71	96.24 1	98.35 234	95.76 87	99.58 23	99.59 24

eth-test2					0.00 434

eth-test					0.00 434

OPU-MVS				98.55 3	98.82 55	96.86 3	98.25 35				98.26 72	96.04 2	99.24 131	95.36 101	99.59 19	99.56 31

test_0728_THIRD									94.78 47	98.73 21	98.87 21	95.87 4	99.84 23	97.45 35	99.72 2	99.77 2

GSMVS																98.45 150

test_part2					99.28 25	95.74 8				98.10 33

sam_mvs1												82.76 202				98.45 150

sam_mvs												81.94 221

test_post1							92.81 379				16.58 427	80.53 242	97.68 314	86.20 289

test_post											17.58 426	81.76 223	98.08 261

patchmatchnet-post											90.45 382	82.65 206	98.10 256

gm-plane-assit					93.22 361	78.89 391			84.82 348		93.52 338		98.64 208	87.72 257

test9_res														94.81 116	99.38 58	99.45 50

agg_prior2														93.94 134	99.38 58	99.50 43

test_prior4						93.66 57	96.42 235

test_prior2							96.35 244		92.80 130	96.03 107	97.59 126	92.01 47		95.01 109	99.38 58

旧先验2							95.94 269		81.66 379	97.34 55			98.82 185	92.26 162

新几何2							95.79 278

原ACMM2							95.67 283

testdata2													99.67 62	85.96 297

segment_acmp												92.89 30

testdata1							95.26 307		93.10 116

plane_prior7					96.21 229	89.98 187

plane_prior6					96.10 240	90.00 183						81.32 229

plane_prior4											96.64 178

plane_prior3						90.00 183			94.46 63	91.34 223

plane_prior2							97.74 98		94.85 40

plane_prior1					96.14 237

plane_prior						89.99 185	97.24 163		94.06 75						92.16 256

HQP5-MVS						89.33 212

HQP-NCC					95.86 246		96.65 218		93.55 91	90.14 247

ACMP_Plane					95.86 246		96.65 218		93.55 91	90.14 247

BP-MVS														92.13 168

HQP4-MVS										90.14 247			98.50 220			95.78 272

HQP2-MVS												80.95 233

NP-MVS					95.99 244	89.81 193					95.87 220

MDTV_nov1_ep13_2view						70.35 407	93.10 374		83.88 359	93.55 168		82.47 210		86.25 288		98.38 158

ACMMP++_ref															90.30 286

ACMMP++															91.02 275

Test By Simon												88.73 99

High-resolution multi-view benchmark