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	85.39 301	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	95.48 246	91.45 124	98.12 48	98.71 11	93.37 91	90.23 231	96.70 162	87.66 110	97.85 284	91.49 171	90.39 272	95.83 250

UniMVSNet (Re)		93.31 150	92.55 164	95.61 143	95.39 251	93.34 64	97.39 139	98.71 11	93.14 103	90.10 240	94.83 260	87.71 109	98.03 256	91.67 169	83.99 340	95.46 273

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	95.70 236	92.03 101	98.10 49	98.68 13	93.36 93	90.39 228	96.70 162	87.63 112	97.94 274	92.25 151	90.50 271	95.84 249

WR-MVS_H		92.00 207	91.35 203	93.95 234	95.09 277	89.47 195	98.04 55	98.68 13	91.46 154	88.34 288	94.68 267	85.86 139	97.56 309	85.77 283	84.24 338	94.82 315

VPA-MVSNet		93.24 152	92.48 169	95.51 149	95.70 236	92.39 87	97.86 79	98.66 16	92.30 130	92.09 189	95.37 238	80.49 230	98.40 209	93.95 120	85.86 312	95.75 260

UniMVSNet_NR-MVSNet		93.37 148	92.67 158	95.47 154	95.34 257	92.83 74	97.17 162	98.58 17	92.98 112	90.13 236	95.80 215	88.37 100	97.85 284	91.71 166	83.93 341	95.73 262

CSCG		96.05 67	95.91 66	96.46 89	99.24 28	90.47 164	98.30 30	98.57 18	89.01 233	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	94.58 307	98.49 19	85.06 327	93.78 148	95.78 219	82.86 187	98.67 187	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	95.18 294	98.48 21	85.60 317	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	89.86 206	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	90.17 199	94.36 134	95.24 244	88.02 104	99.58 77	93.44 131	90.72 267	94.36 335

PVSNet_Blended		94.87 101	94.56 98	95.81 130	98.27 83	89.46 197	95.47 280	98.36 24	88.84 241	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	89.25 270	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	95.48 246	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	97.43 198

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	95.67 238	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
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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	90.21 198	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		91.55 225	90.84 224	93.69 250	94.96 281	88.28 234	97.84 83	98.24 47	91.46 154	88.04 298	95.80 215	79.67 246	97.48 317	87.02 263	84.54 335	95.31 285

DU-MVS		92.90 172	92.04 179	95.49 151	94.95 282	92.83 74	97.16 163	98.24 47	93.02 106	90.13 236	95.71 222	83.47 171	97.85 284	91.71 166	83.93 341	95.78 255

9.14			96.75 41		98.93 47		97.73 95	98.23 50	91.28 162	97.88 35	98.44 44	93.00 26	99.65 58	95.76 73	99.47 38

D2MVS		91.30 240	90.95 218	92.35 293	94.71 299	85.52 297	96.18 245	98.21 51	88.89 239	86.60 325	93.82 310	79.92 242	97.95 273	89.29 214	90.95 263	93.56 348

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	91.79 194	97.28 131	79.13 255	98.93 160	94.61 110	92.84 226	97.28 206

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	89.67 275	97.81 28	99.38 14	94.03 48	98.59 12	98.20 52	94.85 34	96.59 74	32.69 402	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	93.82 242	95.05 278	88.57 225	97.82 86	98.19 55	91.70 147	88.21 294	95.76 220	81.96 207	97.52 315	87.86 238	84.65 330	95.37 281

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	90.57 193	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		91.20 244	90.44 240	93.48 259	94.49 307	87.91 249	97.76 91	98.18 57	91.29 159	87.78 302	95.74 221	80.35 233	97.33 328	85.46 287	82.96 351	95.19 296

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		89.70 293	88.40 298	93.60 253	95.15 273	90.10 172	97.56 119	98.16 61	87.28 291	86.16 329	94.63 270	77.57 282	98.05 252	74.48 367	84.59 333	92.65 361

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	95.27 186	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	97.41 324	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	90.36 191	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	84.50 335	88.27 292	97.77 102	82.39 200	99.81 29	85.40 288	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	95.34 257	92.73 78	98.27 33	98.12 67	84.86 330	85.78 331	97.75 103	78.89 264	99.74 41	87.50 253	98.65 98	96.73 222

TranMVSNet+NR-MVSNet		92.50 183	91.63 194	95.14 164	94.76 294	92.07 99	97.53 123	98.11 70	92.90 116	89.56 258	96.12 200	83.16 177	97.60 307	89.30 213	83.20 350	95.75 260

CPTT-MVS		95.57 80	95.19 83	96.70 71	99.27 26	91.48 121	98.33 28	98.11 70	87.79 276	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	91.50 152	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	89.22 226	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	88.35 259	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	94.95 282	93.05 70	97.39 139	98.07 79	92.65 123	84.46 342	95.71 222	85.00 149	97.77 293	89.71 202	83.52 347	95.78 255

MP-MVS-pluss		96.70 47	96.27 61	97.98 21	99.23 30	94.71 28	96.96 178	98.06 82	90.67 184	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	90.57 193	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	91.17 167	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	88.94 237	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		88.98 298	87.05 312	94.77 191	94.45 309	87.19 264	90.23 379	98.03 91	77.87 381	92.40 175	87.55 384	80.17 237	99.51 96	68.84 385	93.95 214	97.60 192

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	88.17 263	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	88.58 250	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	88.58 250	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	95.13 275	90.83 151	96.40 227	97.98 100	91.88 144	89.29 267	95.54 233	82.50 196	97.80 289	89.79 201	85.27 321	95.69 264

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	77.99 379	93.00 166	97.57 119	86.14 137	99.33 115	89.22 217	99.15 75	98.94 97

IU-MVS					99.42 7	95.39 11		97.94 104	90.40 197	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		90.63 267	89.42 282	94.27 218	98.24 87	89.19 212	98.05 54	97.89 107	79.95 371	88.25 293	94.96 252	72.56 322	98.13 234	89.70 203	85.14 323	95.49 268

原ACMM1				96.38 96	98.59 66	91.09 142		97.89 107	87.41 287	95.22 120	97.68 107	90.25 74	99.54 89	87.95 237	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	86.98 295	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	98.71 184	97.10 31	99.17 73	98.90 102

无先验							95.79 264	97.87 111	83.87 343				99.65 58	87.68 247		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	88.53 285	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	95.56 242	90.96 145	97.22 158	97.86 115	92.96 114	90.96 219	96.62 177	75.06 304	98.20 227	91.90 159	83.65 346	95.80 253

test_vis1_n_1920		94.17 114	94.58 97	92.91 279	97.42 141	82.02 340	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	97.40 200

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	98.75 178	96.92 35	99.33 58	98.94 97

test_fmvsmconf0.01_n		96.15 65	95.85 68	97.03 67	92.66 356	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	89.87 205	92.15 185	97.06 145	83.62 170	99.54 89	89.34 212	98.07 122	97.70 185

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	94.51 275	91.23 61	98.92 161	95.65 78	98.19 118	97.82 180

新几何1				97.32 51	98.60 65	93.59 56		97.75 123	81.58 362	95.75 106	97.85 96	90.04 77	99.67 56	86.50 269	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	98.60 194	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	89.21 218	97.78 129	98.97 93

Anonymous20240529		91.98 208	90.73 230	95.73 136	98.14 99	89.40 199	97.99 60	97.72 128	79.63 373	93.54 153	97.41 127	69.94 339	99.56 85	91.04 181	91.11 259	98.22 153

CHOSEN 280x420		93.12 159	92.72 157	94.34 212	96.71 181	87.27 260	90.29 378	97.72 128	86.61 302	91.34 207	95.29 240	84.29 160	98.41 208	93.25 135	98.94 89	97.35 203

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	85.17 325	90.29 230	98.34 54	84.60 153	99.73 42	83.85 309	98.27 115	98.06 167

PAPR		94.18 113	93.42 133	96.48 86	97.64 129	91.42 125	95.55 275	97.71 132	88.99 234	92.34 181	95.82 214	89.19 85	99.11 138	86.14 275	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	82.73 353	97.03 57	98.07 76	90.06 76	98.85 167	89.67 204	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		90.56 268	89.75 273	93.01 275	93.95 323	87.25 261	97.64 110	97.65 136	90.74 179	87.12 314	95.68 225	79.97 241	97.00 340	83.33 310	81.66 357	94.78 322

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	81.83 359	91.52 202	97.23 136	87.94 106	98.91 163	71.31 380	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	91.79 194	97.28 131	75.35 303	98.65 189	88.99 223	92.84 226	97.28 206

test_cas_vis1_n_1920		94.48 108	94.55 101	94.28 217	96.78 175	86.45 282	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	97.31 205

cdsmvs_eth3d_5k		23.24 372	30.99 374	0.00 390	0.00 413	0.00 415	0.00 401	97.63 140	0.00 408	0.00 409	96.88 155	84.38 157	0.00 409	0.00 408	0.00 407	0.00 405

DPM-MVS		95.69 75	94.92 88	98.01 19	98.08 104	95.71 9	95.27 290	97.62 141	90.43 196	95.55 113	97.07 144	91.72 46	99.50 99	89.62 206	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	94.82 194	98.91 101

test222					98.24 87	92.21 94	95.33 285	97.60 142	79.22 375	95.25 118	97.84 98	88.80 92			99.15 75	98.72 116

cascas		91.20 244	90.08 257	94.58 200	94.97 280	89.16 213	93.65 344	97.59 144	79.90 372	89.40 262	92.92 333	75.36 302	98.36 215	92.14 154	94.75 196	96.23 232

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	79.73 364	98.29 150

MVSFormer		95.37 83	95.16 84	95.99 124	96.34 208	91.21 133	98.22 41	97.57 146	91.42 156	96.22 89	97.32 129	86.20 135	97.92 278	94.07 117	99.05 83	98.85 108

test_djsdf		93.07 163	92.76 152	94.00 229	93.49 339	88.70 222	98.22 41	97.57 146	91.42 156	90.08 242	95.55 232	82.85 188	97.92 278	94.07 117	91.58 247	95.40 278

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	94.44 205	93.91 325	89.28 207	97.75 92	97.56 149	92.50 126	89.94 245	96.54 180	88.65 95	98.18 230	93.83 126	90.90 264	95.86 246

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	98.78 173	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	94.03 228	93.33 345	88.50 229	97.73 95	97.53 151	92.00 142	88.85 277	96.50 182	75.62 301	98.11 240	93.88 124	91.56 248	95.48 269

mvs_tets		92.31 194	91.76 188	93.94 236	93.41 342	88.29 233	97.63 112	97.53 151	92.04 140	88.76 280	96.45 184	74.62 309	98.09 244	93.91 122	91.48 250	95.45 274

dcpmvs_2		96.37 60	97.05 22	94.31 215	98.96 46	84.11 319	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	96.21 212	89.99 176	97.74 93	97.51 153	94.85 34	91.34 207	96.64 168	81.32 217	98.60 194	93.02 142	92.23 235	95.86 246

plane_prior5								97.51 153					98.60 194	93.02 142	92.23 235	95.86 246

PS-MVSNAJ		95.37 83	95.33 80	95.49 151	97.35 142	90.66 160	95.31 287	97.48 156	93.85 72	96.51 77	95.70 224	88.65 95	99.65 58	94.80 104	98.27 115	96.17 236

API-MVS		94.84 102	94.49 103	95.90 126	97.90 115	92.00 102	97.80 89	97.48 156	89.19 227	94.81 126	96.71 160	88.84 91	99.17 131	88.91 225	98.76 95	96.53 225

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	88.13 266	93.00 166	95.84 212	84.86 151	99.51 96	87.99 236	98.17 120	97.83 179
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	94.32 213	96.12 222	89.20 210	95.28 288	97.47 159	92.66 122	89.90 246	95.62 228	80.58 228	98.40 209	92.73 147	92.40 233	95.38 280
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_ETH3D		91.34 238	90.22 253	94.68 194	94.86 290	87.86 250	97.23 157	97.46 161	87.99 267	89.90 246	96.92 153	66.35 361	98.23 224	90.30 192	90.99 262	97.96 170

nrg030		94.05 123	93.31 135	96.27 105	95.22 268	94.59 29	98.34 27	97.46 161	92.93 115	91.21 217	96.64 168	87.23 122	98.22 225	94.99 98	85.80 313	95.98 245

XVG-OURS		93.72 137	93.35 134	94.80 189	97.07 154	88.61 223	94.79 302	97.46 161	91.97 143	93.99 143	97.86 95	81.74 212	98.88 165	92.64 148	92.67 231	96.92 217

LPG-MVS_test		92.94 170	92.56 163	94.10 223	96.16 217	88.26 235	97.65 106	97.46 161	91.29 159	90.12 238	97.16 139	79.05 257	98.73 180	92.25 151	91.89 243	95.31 285

LGP-MVS_train				94.10 223	96.16 217	88.26 235		97.46 161	91.29 159	90.12 238	97.16 139	79.05 257	98.73 180	92.25 151	91.89 243	95.31 285

MVS		91.71 214	90.44 240	95.51 149	95.20 270	91.59 116	96.04 250	97.45 166	73.44 387	87.36 311	95.60 229	85.42 144	99.10 139	85.97 280	97.46 135	95.83 250

XVG-OURS-SEG-HR		93.86 131	93.55 121	94.81 186	97.06 157	88.53 228	95.28 288	97.45 166	91.68 148	94.08 142	97.68 107	82.41 199	98.90 164	93.84 125	92.47 232	96.98 213

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	98.68 186	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	95.40 282	97.45 166	89.81 210	93.22 164	96.28 192	79.62 248	99.46 103	90.74 185	93.11 223	98.50 130

xiu_mvs_v2_base		95.32 85	95.29 81	95.40 156	97.22 144	90.50 163	95.44 281	97.44 170	93.70 77	96.46 81	96.18 196	88.59 98	99.53 91	94.79 106	97.81 128	96.17 236

1314		92.81 178	92.03 180	95.14 164	95.33 260	89.52 194	96.04 250	97.44 170	87.72 280	86.25 328	95.33 239	83.84 165	98.79 172	89.26 215	97.05 152	97.11 211

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	98.73 180	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	94.75 296	90.94 146	97.47 131	97.43 173	89.14 228	88.90 274	96.43 185	79.71 245	98.24 223	89.56 207	87.68 296	95.67 266

anonymousdsp		92.16 202	91.55 197	93.97 232	92.58 358	89.55 191	97.51 124	97.42 174	89.42 221	88.40 287	94.84 259	80.66 226	97.88 283	91.87 161	91.28 255	94.48 330

Effi-MVS+		94.93 98	94.45 105	96.36 98	96.61 185	91.47 122	96.41 223	97.41 175	91.02 173	94.50 132	95.92 208	87.53 114	98.78 173	93.89 123	96.81 155	98.84 110

HQP3-MVS								97.39 176							92.10 240

HQP-MVS		93.19 155	92.74 155	94.54 202	95.86 229	89.33 203	96.65 205	97.39 176	93.55 80	90.14 232	95.87 210	80.95 220	98.50 202	92.13 155	92.10 240	95.78 255

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	87.29 290	91.37 206	96.71 160	88.39 99	99.52 95	87.33 256	97.13 151	97.73 183
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7n		90.76 261	89.86 266	93.45 261	93.54 336	87.60 256	97.70 102	97.37 179	88.85 240	87.65 304	94.08 302	81.08 219	98.10 241	84.68 296	83.79 345	94.66 327

UnsupCasMVSNet_eth		85.99 331	84.45 335	90.62 335	89.97 376	82.40 337	93.62 345	97.37 179	89.86 206	78.59 376	92.37 342	65.25 368	95.35 369	82.27 323	70.75 385	94.10 341

ACMM	89.79 8	92.96 168	92.50 168	94.35 210	96.30 210	88.71 221	97.58 117	97.36 181	91.40 158	90.53 224	96.65 167	79.77 244	98.75 178	91.24 177	91.64 245	95.59 267
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	96.11 241

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	96.11 241

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	96.11 241

diffmvs		95.25 87	95.13 85	95.63 141	96.43 204	89.34 202	95.99 254	97.35 182	92.83 117	96.31 85	97.37 128	86.44 130	98.67 187	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	90.61 190	94.64 129	96.93 150	86.41 131	99.39 111	91.20 178	94.71 198	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	87.75 279	90.49 225	97.10 143	85.21 146	99.50 99	86.70 266	96.72 159	97.63 187

XVG-ACMP-BASELINE		90.93 257	90.21 254	93.09 273	94.31 315	85.89 292	95.33 285	97.26 188	91.06 172	89.38 263	95.44 237	68.61 345	98.60 194	89.46 209	91.05 260	94.79 320

PCF-MVS	89.48 11	91.56 224	89.95 263	96.36 98	96.60 186	92.52 84	92.51 364	97.26 188	79.41 374	88.90 274	96.56 179	84.04 164	99.55 87	77.01 358	97.30 145	97.01 212
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	94.05 226	96.40 205	88.20 238	97.36 142	97.25 190	91.52 151	88.30 290	96.64 168	78.46 269	98.72 183	91.86 162	91.48 250	95.23 292
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	94.63 302	90.77 154	96.65 205	97.18 191	93.72 75	91.68 198	97.26 134	79.33 252	98.63 191	92.13 155	92.28 234	95.07 298
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	95.27 290	97.18 191	87.96 268	91.86 193	95.68 225	80.44 231	98.99 156	84.01 304	97.54 134	96.89 218

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	95.11 190	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	91.17 167	95.60 112	96.11 203	87.87 108	98.76 177	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	88.19 262	91.30 210	94.27 291	86.21 134	98.63 191	87.66 248	96.46 166	98.12 161

EI-MVSNet		93.03 165	92.88 147	93.48 259	95.77 234	86.98 269	96.44 219	97.12 196	90.66 186	91.30 210	97.64 114	86.56 127	98.05 252	89.91 197	90.55 269	95.41 275

MVSTER		93.20 154	92.81 151	94.37 209	96.56 191	89.59 189	97.06 168	97.12 196	91.24 163	91.30 210	95.96 206	82.02 206	98.05 252	93.48 130	90.55 269	95.47 272

test_yl		94.78 104	94.23 108	96.43 91	97.74 122	91.22 131	96.85 185	97.10 198	91.23 164	95.71 107	96.93 150	84.30 158	99.31 119	93.10 137	95.12 188	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	91.23 164	95.71 107	96.93 150	84.30 158	99.31 119	93.10 137	95.12 188	98.75 113

LTVRE_ROB	88.41 13	90.99 253	89.92 265	94.19 219	96.18 215	89.55 191	96.31 235	97.09 200	87.88 271	85.67 332	95.91 209	78.79 265	98.57 198	81.50 326	89.98 275	94.44 333
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	92.29 296	96.08 225	81.05 348	97.98 61	97.08 201	90.72 181	96.79 62	98.18 70	63.07 372	98.45 206	97.62 20	98.42 110	97.36 201

v10		91.04 251	90.23 251	93.49 258	94.12 319	88.16 241	97.32 147	97.08 201	88.26 261	88.29 291	94.22 296	82.17 204	97.97 264	86.45 270	84.12 339	94.33 336

v144192		91.06 250	90.28 247	93.39 262	93.66 334	87.23 263	96.83 188	97.07 203	87.43 286	89.69 253	94.28 290	81.48 215	98.00 259	87.18 260	84.92 329	94.93 306

v1192		91.07 249	90.23 251	93.58 255	93.70 331	87.82 252	96.73 195	97.07 203	87.77 277	89.58 256	94.32 288	80.90 224	97.97 264	86.52 268	85.48 316	94.95 302

v8		91.29 241	90.53 239	93.57 256	94.15 318	88.12 242	97.34 144	97.06 205	88.99 234	88.32 289	94.26 293	83.08 180	98.01 258	87.62 250	83.92 343	94.57 329

mvs_anonymous		93.82 133	93.74 115	94.06 225	96.44 203	85.41 299	95.81 262	97.05 206	89.85 208	90.09 241	96.36 189	87.44 117	97.75 294	93.97 119	96.69 160	99.02 86

IterMVS-LS		92.29 196	91.94 184	93.34 264	96.25 211	86.97 270	96.57 217	97.05 206	90.67 184	89.50 261	94.80 262	86.59 126	97.64 302	89.91 197	86.11 311	95.40 278
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		90.85 259	90.03 262	93.29 266	93.55 335	86.96 271	96.74 194	97.04 208	87.36 288	89.52 260	94.34 285	80.23 236	97.97 264	86.27 271	85.21 322	94.94 304

CDS-MVSNet		94.14 119	93.54 122	95.93 125	96.18 215	91.46 123	96.33 233	97.04 208	88.97 236	93.56 151	96.51 181	87.55 113	97.89 282	89.80 200	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		91.37 235	90.60 235	93.68 251	93.89 326	88.23 237	96.84 187	97.03 210	88.37 258	89.69 253	94.39 282	82.04 205	97.98 261	87.80 240	85.37 318	94.84 312

v1240		90.70 265	89.85 267	93.23 268	93.51 338	86.80 272	96.61 211	97.02 211	87.16 293	89.58 256	94.31 289	79.55 249	97.98 261	85.52 286	85.44 317	94.90 309

EPP-MVSNet		95.22 89	95.04 87	95.76 131	97.49 139	89.56 190	98.67 10	97.00 212	90.69 182	94.24 137	97.62 116	89.79 81	98.81 171	93.39 134	96.49 164	98.92 100

V42		91.58 223	90.87 220	93.73 246	94.05 322	88.50 229	97.32 147	96.97 213	88.80 246	89.71 251	94.33 286	82.54 195	98.05 252	89.01 222	85.07 325	94.64 328

test_fmvs1		93.21 153	93.53 123	92.25 298	96.55 193	81.20 347	97.40 138	96.96 214	90.68 183	96.80 61	98.04 79	69.25 341	98.40 209	97.58 21	98.50 104	97.16 210

FMVSNet2		91.31 239	90.08 257	94.99 173	96.51 197	92.21 94	97.41 134	96.95 215	88.82 243	88.62 282	94.75 264	73.87 313	97.42 323	85.20 291	88.55 290	95.35 282

ACMH	87.59 16	90.53 269	89.42 282	93.87 240	96.21 212	87.92 247	97.24 153	96.94 216	88.45 256	83.91 352	96.27 193	71.92 323	98.62 193	84.43 299	89.43 281	95.05 300
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		91.35 236	90.27 248	94.59 196	96.51 197	91.18 137	97.50 125	96.93 217	88.82 243	89.35 264	94.51 275	73.87 313	97.29 330	86.12 276	88.82 285	95.31 285

test1		91.35 236	90.27 248	94.59 196	96.51 197	91.18 137	97.50 125	96.93 217	88.82 243	89.35 264	94.51 275	73.87 313	97.29 330	86.12 276	88.82 285	95.31 285

FMVSNet3		91.78 212	90.69 233	95.03 171	96.53 195	92.27 93	97.02 171	96.93 217	89.79 211	89.35 264	94.65 269	77.01 285	97.47 318	86.12 276	88.82 285	95.35 282

FMVSNet1		89.88 288	88.31 299	94.59 196	95.41 250	91.18 137	97.50 125	96.93 217	86.62 301	87.41 309	94.51 275	65.94 365	97.29 330	83.04 313	87.43 299	95.31 285

GeoE		93.89 129	93.28 136	95.72 137	96.96 165	89.75 184	98.24 39	96.92 221	89.47 219	92.12 187	97.21 137	84.42 156	98.39 213	87.71 243	96.50 163	99.01 89

miper_enhance_ethall		91.54 226	91.01 217	93.15 271	95.35 256	87.07 268	93.97 330	96.90 222	86.79 299	89.17 271	93.43 328	86.55 128	97.64 302	89.97 196	86.93 303	94.74 324

eth_miper_zixun_eth		91.02 252	90.59 236	92.34 295	95.33 260	84.35 315	94.10 327	96.90 222	88.56 252	88.84 278	94.33 286	84.08 163	97.60 307	88.77 228	84.37 337	95.06 299

TAMVS		94.01 125	93.46 128	95.64 140	96.16 217	90.45 165	96.71 198	96.89 224	89.27 225	93.46 156	96.92 153	87.29 120	97.94 274	88.70 229	95.74 176	98.53 126

miper_ehance_all_eth		91.59 221	91.13 215	92.97 277	95.55 243	86.57 280	94.47 311	96.88 225	87.77 277	88.88 276	94.01 303	86.22 133	97.54 311	89.49 208	86.93 303	94.79 320

v2v482		91.59 221	90.85 223	93.80 243	93.87 327	88.17 240	96.94 179	96.88 225	89.54 216	89.53 259	94.90 256	81.70 213	98.02 257	89.25 216	85.04 327	95.20 293

CNLPA		94.28 111	93.53 123	96.52 80	98.38 78	92.55 83	96.59 214	96.88 225	90.13 202	91.91 191	97.24 135	85.21 146	99.09 142	87.64 249	97.83 127	97.92 172

PAPM		91.52 227	90.30 246	95.20 161	95.30 263	89.83 182	93.38 350	96.85 228	86.26 308	88.59 283	95.80 215	84.88 150	98.15 232	75.67 363	95.93 172	97.63 187

c3_l		91.38 233	90.89 219	92.88 281	95.58 241	86.30 285	94.68 304	96.84 229	88.17 263	88.83 279	94.23 294	85.65 142	97.47 318	89.36 211	84.63 331	94.89 310

pm-mvs1		90.72 264	89.65 277	93.96 233	94.29 316	89.63 186	97.79 90	96.82 230	89.07 230	86.12 330	95.48 236	78.61 267	97.78 291	86.97 264	81.67 356	94.46 331

test_vis1_n		92.37 190	92.26 175	92.72 286	94.75 296	82.64 332	98.02 56	96.80 231	91.18 166	97.77 37	97.93 88	58.02 380	98.29 221	97.63 19	98.21 117	97.23 209

CMPMVS	62.92 21	85.62 335	84.92 332	87.74 356	89.14 381	73.12 386	94.17 325	96.80 231	73.98 385	73.65 384	94.93 254	66.36 360	97.61 306	83.95 306	91.28 255	92.48 365
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MS-PatchMatch		90.27 276	89.77 271	91.78 311	94.33 313	84.72 313	95.55 275	96.73 233	86.17 310	86.36 327	95.28 242	71.28 328	97.80 289	84.09 303	98.14 121	92.81 358

Effi-MVS+-dtu		93.08 162	93.21 138	92.68 289	96.02 226	83.25 329	97.14 165	96.72 234	93.85 72	91.20 218	93.44 325	83.08 180	98.30 220	91.69 168	95.73 177	96.50 227

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	86.87 298	89.83 249	96.69 164	86.51 129	99.14 135	88.12 234	93.67 217	98.50 130

PVSNet	86.66 18	92.24 199	91.74 191	93.73 246	97.77 121	83.69 326	92.88 359	96.72 234	87.91 270	93.00 166	94.86 258	78.51 268	99.05 151	86.53 267	97.45 139	98.47 135

miper_lstm_enhance		90.50 272	90.06 261	91.83 307	95.33 260	83.74 323	93.86 336	96.70 238	87.56 284	87.79 301	93.81 311	83.45 173	96.92 342	87.39 254	84.62 332	94.82 315

v148		90.99 253	90.38 242	92.81 284	93.83 328	85.80 293	96.78 192	96.68 239	89.45 220	88.75 281	93.93 307	82.96 186	97.82 288	87.83 239	83.25 348	94.80 318

ACMH+	87.92 14	90.20 280	89.18 287	93.25 267	96.48 200	86.45 282	96.99 175	96.68 239	88.83 242	84.79 341	96.22 195	70.16 336	98.53 200	84.42 300	88.04 293	94.77 323

CANet_DTU		94.37 109	93.65 118	96.55 78	96.46 202	92.13 98	96.21 243	96.67 241	94.38 58	93.53 154	97.03 147	79.34 251	99.71 46	90.76 184	98.45 109	97.82 180

cl____		90.96 256	90.32 244	92.89 280	95.37 254	86.21 288	94.46 313	96.64 242	87.82 273	88.15 296	94.18 297	82.98 184	97.54 311	87.70 244	85.59 314	94.92 308

HY-MVS	89.66 9	93.87 130	92.95 144	96.63 74	97.10 153	92.49 85	95.64 273	96.64 242	89.05 232	93.00 166	95.79 218	85.77 141	99.45 105	89.16 221	94.35 200	97.96 170

Test_1112_low_res		92.84 176	91.84 187	95.85 129	97.04 160	89.97 179	95.53 277	96.64 242	85.38 320	89.65 255	95.18 245	85.86 139	99.10 139	87.70 244	93.58 222	98.49 132

DIV-MVS_self_test		90.97 255	90.33 243	92.88 281	95.36 255	86.19 289	94.46 313	96.63 245	87.82 273	88.18 295	94.23 294	82.99 183	97.53 313	87.72 241	85.57 315	94.93 306

Fast-Effi-MVS+-dtu		92.29 196	91.99 182	93.21 270	95.27 264	85.52 297	97.03 169	96.63 245	92.09 138	89.11 273	95.14 247	80.33 234	98.08 245	87.54 252	94.74 197	96.03 244

UnsupCasMVSNet_bld		82.13 348	79.46 353	90.14 341	88.00 386	82.47 335	90.89 376	96.62 247	78.94 376	75.61 380	84.40 389	56.63 383	96.31 351	77.30 355	66.77 392	91.63 372

cl22		91.21 243	90.56 238	93.14 272	96.09 224	86.80 272	94.41 315	96.58 248	87.80 275	88.58 284	93.99 305	80.85 225	97.62 305	89.87 199	86.93 303	94.99 301

RRT_MVS		93.10 160	92.83 149	93.93 238	94.76 294	88.04 243	98.47 22	96.55 249	93.44 88	90.01 244	97.04 146	80.64 227	97.93 277	94.33 114	90.21 274	95.83 250

jason		94.84 102	94.39 107	96.18 112	95.52 244	90.93 147	96.09 248	96.52 250	89.28 224	96.01 98	97.32 129	84.70 152	98.77 176	95.15 94	98.91 91	98.85 108
jason: jason.
tt0805		91.09 248	90.07 260	94.16 221	95.61 239	88.31 232	97.56 119	96.51 251	89.56 215	89.17 271	95.64 227	67.08 359	98.38 214	91.07 180	88.44 291	95.80 253

AUN-MVS		91.76 213	90.75 228	94.81 186	97.00 163	88.57 225	96.65 205	96.49 252	89.63 213	92.15 185	96.12 200	78.66 266	98.50 202	90.83 182	79.18 367	97.36 201

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	98.48 205	95.85 69	79.13 368	97.35 203

EG-PatchMatch MVS		87.02 321	85.44 325	91.76 313	92.67 355	85.00 308	96.08 249	96.45 254	83.41 349	79.52 372	93.49 323	57.10 382	97.72 296	79.34 346	90.87 266	92.56 362

KD-MVS_self_test		85.95 332	84.95 331	88.96 351	89.55 380	79.11 371	95.13 295	96.42 255	85.91 313	84.07 350	90.48 362	70.03 338	94.82 371	80.04 338	72.94 382	92.94 356

pmmvs6		87.81 313	86.19 320	92.69 288	91.32 368	86.30 285	97.34 144	96.41 256	80.59 370	84.05 351	94.37 284	67.37 354	97.67 299	84.75 295	79.51 366	94.09 343

PMMVS		92.86 174	92.34 172	94.42 207	94.92 285	86.73 275	94.53 309	96.38 257	84.78 332	94.27 136	95.12 249	83.13 179	98.40 209	91.47 172	96.49 164	98.12 161

RPSCF		90.75 262	90.86 221	90.42 338	96.84 169	76.29 379	95.61 274	96.34 258	83.89 341	91.38 205	97.87 93	76.45 290	98.78 173	87.16 261	92.23 235	96.20 234

MSDG		91.42 231	90.24 250	94.96 177	97.15 150	88.91 217	93.69 342	96.32 259	85.72 316	86.93 322	96.47 183	80.24 235	98.98 157	80.57 335	95.05 191	96.98 213

OurMVSNet-221017-0		90.51 271	90.19 255	91.44 319	93.41 342	81.25 345	96.98 176	96.28 260	91.68 148	86.55 326	96.30 191	74.20 312	97.98 261	88.96 224	87.40 301	95.09 297

MVP-Stereo		90.74 263	90.08 257	92.71 287	93.19 347	88.20 238	95.86 260	96.27 261	86.07 311	84.86 340	94.76 263	77.84 280	97.75 294	83.88 308	98.01 123	92.17 370
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	96.34 208	91.21 133	95.83 261	96.27 261	88.93 238	96.22 89	96.88 155	86.20 135	98.85 167	95.27 91	99.05 83	98.82 111

BH-untuned		92.94 170	92.62 161	93.92 239	97.22 144	86.16 290	96.40 227	96.25 263	90.06 203	89.79 250	96.17 198	83.19 176	98.35 216	87.19 259	97.27 146	97.24 208

CL-MVSNet_self_test		86.31 327	85.15 329	89.80 345	88.83 383	81.74 343	93.93 333	96.22 264	86.67 300	85.03 338	90.80 361	78.09 276	94.50 372	74.92 366	71.86 384	93.15 354

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	98.45 206	89.98 195	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	94.82 301	96.21 266	89.61 214	94.20 138	95.25 243	83.24 175	99.14 135	90.01 194	96.16 168	98.25 151

GA-MVS		91.38 233	90.31 245	94.59 196	94.65 301	87.62 255	94.34 318	96.19 267	90.73 180	90.35 229	93.83 308	71.84 324	97.96 269	87.22 258	93.61 220	98.21 154

IterMVS-SCA-FT		90.31 274	89.81 269	91.82 308	95.52 244	84.20 318	94.30 321	96.15 268	90.61 190	87.39 310	94.27 291	75.80 298	96.44 349	87.34 255	86.88 307	94.82 315

IterMVS		90.15 282	89.67 275	91.61 315	95.48 246	83.72 324	94.33 319	96.12 269	89.99 204	87.31 313	94.15 299	75.78 300	96.27 352	86.97 264	86.89 306	94.83 313
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	82.38 355	89.29 267	97.87 93	83.77 166	99.69 52	81.37 331	96.69 160	98.89 105

pmmvs4		90.93 257	89.85 267	94.17 220	93.34 344	90.79 153	94.60 306	96.02 271	84.62 333	87.45 307	95.15 246	81.88 210	97.45 320	87.70 244	87.87 295	94.27 340

ppachtmachnet_test		88.35 308	87.29 307	91.53 316	92.45 361	83.57 327	93.75 339	95.97 272	84.28 336	85.32 337	94.18 297	79.00 263	96.93 341	75.71 362	84.99 328	94.10 341

Anonymous20240521		86.42 325	85.44 325	89.34 349	90.33 373	79.79 363	96.73 195	95.92 273	83.71 345	83.25 355	91.36 358	63.92 370	96.01 353	78.39 350	85.36 319	92.22 368

ITE_SJBPF				92.43 292	95.34 257	85.37 302		95.92 273	91.47 153	87.75 303	96.39 188	71.00 330	97.96 269	82.36 322	89.86 277	93.97 344

test_fmvs2		89.77 292	89.93 264	89.31 350	93.68 333	76.37 378	97.64 110	95.90 275	89.84 209	91.49 203	96.26 194	58.77 379	97.10 334	94.65 108	91.13 258	94.46 331

USDC		88.94 299	87.83 304	92.27 297	94.66 300	84.96 309	93.86 336	95.90 275	87.34 289	83.40 354	95.56 231	67.43 353	98.19 229	82.64 321	89.67 279	93.66 347

COLMAP_ROB	87.81 15	90.40 273	89.28 285	93.79 244	97.95 110	87.13 267	96.92 180	95.89 277	82.83 352	86.88 324	97.18 138	73.77 316	99.29 121	78.44 349	93.62 219	94.95 302
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	68.42 349	99.39 111	96.31 49	94.85 192	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	86.22 309	95.79 105	98.46 42	67.59 352	99.19 128	94.92 99	94.85 192	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	97.91 281	88.24 233	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		84.81 339	82.64 343	91.31 321	91.07 370	85.34 303	91.22 371	95.75 282	85.56 318	83.09 356	90.21 365	67.21 355	95.89 355	77.18 356	62.48 395	92.69 359

miper_refine_blended		84.81 339	82.64 343	91.31 321	91.07 370	85.34 303	91.22 371	95.75 282	85.56 318	83.09 356	90.21 365	67.21 355	95.89 355	77.18 356	62.48 395	92.69 359

FE-MVS		92.05 206	91.05 216	95.08 167	96.83 171	87.93 246	93.91 335	95.70 284	86.30 306	94.15 140	94.97 251	76.59 288	99.21 126	84.10 302	96.86 153	98.09 165

tpm cat1		88.36 307	87.21 310	91.81 309	95.13 275	80.55 354	92.58 363	95.70 284	74.97 384	87.45 307	91.96 352	78.01 279	98.17 231	80.39 337	88.74 288	96.72 223

our_test_3		88.78 303	87.98 303	91.20 325	92.45 361	82.53 334	93.61 346	95.69 286	85.77 315	84.88 339	93.71 313	79.99 240	96.78 347	79.47 343	86.24 308	94.28 339

BH-w/o		92.14 204	91.75 189	93.31 265	96.99 164	85.73 294	95.67 269	95.69 286	88.73 248	89.26 269	94.82 261	82.97 185	98.07 249	85.26 290	96.32 167	96.13 240

CR-MVSNet		90.82 260	89.77 271	93.95 234	94.45 309	87.19 264	90.23 379	95.68 288	86.89 297	92.40 175	92.36 345	80.91 222	97.05 336	81.09 334	93.95 214	97.60 192

Patchmtry		88.64 305	87.25 308	92.78 285	94.09 320	86.64 276	89.82 382	95.68 288	80.81 367	87.63 305	92.36 345	80.91 222	97.03 337	78.86 347	85.12 324	94.67 326

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	91.60 199	96.64 168	79.28 253	98.13 234	95.34 90	91.49 249	95.70 263

BH-RMVSNet		92.72 181	91.97 183	94.97 176	97.16 148	87.99 245	96.15 246	95.60 291	90.62 189	91.87 192	97.15 141	78.41 270	98.57 198	83.16 311	97.60 133	98.36 147

PVSNet_0	82.17 19	85.46 336	83.64 339	90.92 328	95.27 264	79.49 367	90.55 377	95.60 291	83.76 344	83.00 358	89.95 367	71.09 329	97.97 264	82.75 319	60.79 397	95.31 285

SCA		91.84 211	91.18 214	93.83 241	95.59 240	84.95 310	94.72 303	95.58 293	90.82 176	92.25 183	93.69 314	75.80 298	98.10 241	86.20 273	95.98 170	98.45 137

AllTest		90.23 278	88.98 290	93.98 230	97.94 111	86.64 276	96.51 218	95.54 294	85.38 320	85.49 334	96.77 158	70.28 334	99.15 133	80.02 339	92.87 224	96.15 238

TestCases				93.98 230	97.94 111	86.64 276		95.54 294	85.38 320	85.49 334	96.77 158	70.28 334	99.15 133	80.02 339	92.87 224	96.15 238

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	91.58 200	96.64 168	76.35 293	98.13 234	95.43 88	91.42 252	95.68 265

mvsmamba		93.83 132	93.46 128	94.93 181	94.88 289	90.85 150	98.55 14	95.49 297	94.24 61	91.29 213	96.97 149	83.04 182	98.14 233	95.56 86	91.17 257	95.78 255

tpmvs		89.83 291	89.15 288	91.89 305	94.92 285	80.30 358	93.11 355	95.46 298	86.28 307	88.08 297	92.65 335	80.44 231	98.52 201	81.47 327	89.92 276	96.84 219

pmmvs5		89.86 290	88.87 293	92.82 283	92.86 351	86.23 287	96.26 238	95.39 299	84.24 337	87.12 314	94.51 275	74.27 311	97.36 327	87.61 251	87.57 297	94.86 311

PatchmatchNet		91.91 209	91.35 203	93.59 254	95.38 252	84.11 319	93.15 354	95.39 299	89.54 216	92.10 188	93.68 316	82.82 189	98.13 234	84.81 294	95.32 185	98.52 127
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		91.44 230	91.32 205	91.79 310	95.15 273	79.20 370	93.42 349	95.37 301	88.55 253	93.49 155	93.67 317	82.49 197	98.27 222	90.41 189	89.34 282	97.90 173

Anonymous20231206		87.09 320	86.14 321	89.93 344	91.22 369	80.35 356	96.11 247	95.35 302	83.57 347	84.16 346	93.02 331	73.54 318	95.61 363	72.16 377	86.14 310	93.84 346

MIMVSNet1		84.93 338	83.05 340	90.56 336	89.56 379	84.84 312	95.40 282	95.35 302	83.91 340	80.38 368	92.21 349	57.23 381	93.34 384	70.69 383	82.75 354	93.50 349

TDRefinement		86.53 323	84.76 334	91.85 306	82.23 396	84.25 316	96.38 229	95.35 302	84.97 329	84.09 349	94.94 253	65.76 366	98.34 219	84.60 298	74.52 378	92.97 355

TR-MVS		91.48 229	90.59 236	94.16 221	96.40 205	87.33 257	95.67 269	95.34 305	87.68 281	91.46 204	95.52 234	76.77 287	98.35 216	82.85 316	93.61 220	96.79 221

EPNet_dtu		91.71 214	91.28 208	92.99 276	93.76 330	83.71 325	96.69 201	95.28 306	93.15 102	87.02 318	95.95 207	83.37 174	97.38 326	79.46 344	96.84 154	97.88 175
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet5		87.29 317	85.79 323	91.78 311	94.80 293	87.28 259	95.49 279	95.28 306	84.09 339	83.85 353	91.82 353	62.95 373	94.17 376	78.48 348	85.34 320	93.91 345

MDTV_nov1_ep13			90.76 227		95.22 268	80.33 357	93.03 357	95.28 306	88.14 265	92.84 172	93.83 308	81.34 216	98.08 245	82.86 314	94.34 201

LF4IMVS		87.94 311	87.25 308	89.98 343	92.38 363	80.05 362	94.38 316	95.25 309	87.59 283	84.34 343	94.74 265	64.31 369	97.66 301	84.83 293	87.45 298	92.23 367

TransMVSNet (Re)		88.94 299	87.56 305	93.08 274	94.35 312	88.45 231	97.73 95	95.23 310	87.47 285	84.26 345	95.29 240	79.86 243	97.33 328	79.44 345	74.44 379	93.45 351

test20.03		86.14 330	85.40 327	88.35 352	90.12 374	80.06 361	95.90 259	95.20 311	88.59 249	81.29 363	93.62 319	71.43 327	92.65 386	71.26 381	81.17 359	92.34 366

new-patchmatchnet		83.18 345	81.87 348	87.11 359	86.88 388	75.99 380	93.70 340	95.18 312	85.02 328	77.30 379	88.40 377	65.99 364	93.88 381	74.19 371	70.18 386	91.47 376

MDA-MVSNet_test_wron		85.87 333	84.23 337	90.80 333	92.38 363	82.57 333	93.17 352	95.15 313	82.15 356	67.65 388	92.33 348	78.20 272	95.51 366	77.33 353	79.74 363	94.31 338

YYNet1		85.87 333	84.23 337	90.78 334	92.38 363	82.46 336	93.17 352	95.14 314	82.12 357	67.69 387	92.36 345	78.16 275	95.50 367	77.31 354	79.73 364	94.39 334

Baseline_NR-MVSNet		91.20 244	90.62 234	92.95 278	93.83 328	88.03 244	97.01 174	95.12 315	88.42 257	89.70 252	95.13 248	83.47 171	97.44 321	89.66 205	83.24 349	93.37 352

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	94.00 304	78.39 271	99.04 154	81.26 333	94.18 205	96.19 235

ADS-MVSNet		89.89 287	88.68 295	93.53 257	95.86 229	84.89 311	90.93 374	95.07 317	83.23 350	91.28 214	91.81 354	79.01 261	97.85 284	79.52 341	91.39 253	97.84 177

pmmvs-eth3d		86.22 328	84.45 335	91.53 316	88.34 385	87.25 261	94.47 311	95.01 318	83.47 348	79.51 373	89.61 370	69.75 340	95.71 360	83.13 312	76.73 375	91.64 371

Anonymous202405211		92.07 205	90.83 225	95.76 131	98.19 95	88.75 220	97.58 117	95.00 319	86.00 312	93.64 150	97.45 124	66.24 363	99.53 91	90.68 187	92.71 229	99.01 89

MDA-MVSNet-bldmvs		85.00 337	82.95 342	91.17 326	93.13 349	83.33 328	94.56 308	95.00 319	84.57 334	65.13 392	92.65 335	70.45 333	95.85 357	73.57 373	77.49 371	94.33 336

ambc				86.56 362	83.60 393	70.00 389	85.69 391	94.97 321		80.60 367	88.45 376	37.42 395	96.84 345	82.69 320	75.44 377	92.86 357

testgi		87.97 310	87.21 310	90.24 340	92.86 351	80.76 349	96.67 204	94.97 321	91.74 146	85.52 333	95.83 213	62.66 374	94.47 374	76.25 360	88.36 292	95.48 269

dp		88.90 301	88.26 301	90.81 331	94.58 305	76.62 377	92.85 360	94.93 323	85.12 326	90.07 243	93.07 330	75.81 297	98.12 239	80.53 336	87.42 300	97.71 184

test_fmvs3		83.21 344	83.02 341	83.78 366	86.77 389	68.34 392	96.76 193	94.91 324	86.49 303	84.14 348	89.48 371	36.04 396	91.73 388	91.86 162	80.77 361	91.26 378

test_0402		86.46 324	84.79 333	91.45 318	95.02 279	85.55 296	96.29 237	94.89 325	80.90 364	82.21 360	93.97 306	68.21 350	97.29 330	62.98 389	88.68 289	91.51 374

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	94.46 280	78.17 273	99.08 144	81.40 328	94.08 209	96.48 228

CVMVSNet		91.23 242	91.75 189	89.67 346	95.77 234	74.69 381	96.44 219	94.88 326	85.81 314	92.18 184	97.64 114	79.07 256	95.58 365	88.06 235	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	94.46 280	78.17 273	99.08 144	81.40 328	94.08 209	96.98 213

EPNet		95.20 90	94.56 98	97.14 63	92.80 353	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
testing99		91.62 219	90.72 231	94.32 213	96.48 200	86.11 291	95.81 262	94.76 330	91.55 150	91.75 196	93.44 325	68.55 347	98.82 169	90.43 188	93.69 216	98.04 168

SixPastTwentyTwo		89.15 297	88.54 297	90.98 327	93.49 339	80.28 359	96.70 199	94.70 331	90.78 177	84.15 347	95.57 230	71.78 325	97.71 297	84.63 297	85.07 325	94.94 304

thres100view900		92.43 186	91.58 196	94.98 175	97.92 113	89.37 201	97.71 100	94.66 332	92.20 133	93.31 160	94.90 256	78.06 277	99.08 144	81.40 328	94.08 209	96.48 228

thres600view7		92.49 185	91.60 195	95.18 162	97.91 114	89.47 195	97.65 106	94.66 332	92.18 137	93.33 159	94.91 255	78.06 277	99.10 139	81.61 325	94.06 213	96.98 213

PatchT		88.87 302	87.42 306	93.22 269	94.08 321	85.10 307	89.51 383	94.64 334	81.92 358	92.36 178	88.15 380	80.05 239	97.01 339	72.43 376	93.65 218	97.54 195

baseline1		92.82 177	91.90 185	95.55 147	97.20 146	90.77 154	97.19 160	94.58 335	92.20 133	92.36 178	96.34 190	84.16 162	98.21 226	89.20 219	83.90 344	97.68 186

Gipuma		67.86 363	65.41 365	75.18 379	92.66 356	73.45 384	66.50 398	94.52 336	53.33 397	57.80 398	66.07 398	30.81 398	89.20 392	48.15 398	78.88 370	62.90 398
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testing11		91.68 217	90.75 228	94.47 203	96.53 195	86.56 281	95.76 266	94.51 337	91.10 171	91.24 216	93.59 320	68.59 346	98.86 166	91.10 179	94.29 202	98.00 169

CostFormer		91.18 247	90.70 232	92.62 290	94.84 291	81.76 342	94.09 328	94.43 338	84.15 338	92.72 173	93.77 312	79.43 250	98.20 227	90.70 186	92.18 238	97.90 173

tpm2		89.96 284	89.21 286	92.23 299	94.91 287	81.25 345	93.78 338	94.42 339	80.62 369	91.56 201	93.44 325	76.44 291	97.94 274	85.60 285	92.08 242	97.49 196

JIA-IIPM		88.26 309	87.04 313	91.91 304	93.52 337	81.42 344	89.38 384	94.38 340	80.84 366	90.93 220	80.74 391	79.22 254	97.92 278	82.76 318	91.62 246	96.38 231

dmvs_re		90.21 279	89.50 280	92.35 293	95.47 249	85.15 305	95.70 268	94.37 341	90.94 175	88.42 286	93.57 321	74.63 308	95.67 362	82.80 317	89.57 280	96.22 233

Patchmatch-test		89.42 295	87.99 302	93.70 249	95.27 264	85.11 306	88.98 385	94.37 341	81.11 363	87.10 316	93.69 314	82.28 201	97.50 316	74.37 369	94.76 195	98.48 134

LCM-MVSNet		72.55 357	69.39 361	82.03 368	70.81 406	65.42 397	90.12 381	94.36 343	55.02 396	65.88 390	81.72 390	24.16 404	89.96 389	74.32 370	68.10 390	90.71 381

ADS-MVSNet2		89.45 294	88.59 296	92.03 302	95.86 229	82.26 338	90.93 374	94.32 344	83.23 350	91.28 214	91.81 354	79.01 261	95.99 354	79.52 341	91.39 253	97.84 177

EU-MVSNet		88.72 304	88.90 292	88.20 354	93.15 348	74.21 382	96.63 210	94.22 345	85.18 324	87.32 312	95.97 205	76.16 294	94.98 370	85.27 289	86.17 309	95.41 275

MVS_0304		97.04 28	96.73 42	97.96 23	97.60 134	94.36 34	98.01 57	94.09 346	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		88.50 306	86.76 316	93.72 248	94.84 291	87.77 253	91.39 369	94.05 347	86.41 305	87.99 299	92.59 338	63.27 371	95.82 359	77.44 352	92.84 226	97.57 194

OpenMVS_ROB	81.14 20	84.42 341	82.28 347	90.83 329	90.06 375	84.05 321	95.73 267	94.04 348	73.89 386	80.17 371	91.53 357	59.15 378	97.64 302	66.92 387	89.05 284	90.80 380

TinyColmap		86.82 322	85.35 328	91.21 323	94.91 287	82.99 331	93.94 332	94.02 349	83.58 346	81.56 362	94.68 267	62.34 375	98.13 234	75.78 361	87.35 302	92.52 364

ETVMVS		90.52 270	89.14 289	94.67 195	96.81 174	87.85 251	95.91 258	93.97 350	89.71 212	92.34 181	92.48 340	65.41 367	97.96 269	81.37 331	94.27 203	98.21 154

IB-MVS	87.33 17	89.91 285	88.28 300	94.79 190	95.26 267	87.70 254	95.12 296	93.95 351	89.35 223	87.03 317	92.49 339	70.74 332	99.19 128	89.18 220	81.37 358	97.49 196
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		87.13 319	87.02 314	87.47 357	95.16 271	73.21 385	95.00 297	93.93 352	88.55 253	86.96 319	91.99 350	75.90 295	94.00 378	61.59 391	94.11 206	95.20 293

myMVS_eth3d		87.18 318	86.38 318	89.58 347	95.16 271	79.53 365	95.00 297	93.93 352	88.55 253	86.96 319	91.99 350	56.23 384	94.00 378	75.47 365	94.11 206	95.20 293

testing222		90.31 274	88.96 291	94.35 210	96.54 194	87.29 258	95.50 278	93.84 354	90.97 174	91.75 196	92.96 332	62.18 376	98.00 259	82.86 314	94.08 209	97.76 182

test_f		80.57 350	79.62 352	83.41 367	83.38 394	67.80 394	93.57 347	93.72 355	80.80 368	77.91 378	87.63 383	33.40 397	92.08 387	87.14 262	79.04 369	90.34 382

LCM-MVSNet-Re		92.50 183	92.52 167	92.44 291	96.82 173	81.89 341	96.92 180	93.71 356	92.41 128	84.30 344	94.60 271	85.08 148	97.03 337	91.51 170	97.36 141	98.40 143

bld_raw_dy_0_64		92.37 190	91.69 192	94.39 208	94.28 317	89.73 185	97.71 100	93.65 357	92.78 120	90.46 226	96.67 166	75.88 296	97.97 264	92.92 146	90.89 265	95.48 269

tpm		90.25 277	89.74 274	91.76 313	93.92 324	79.73 364	93.98 329	93.54 358	88.28 260	91.99 190	93.25 329	77.51 283	97.44 321	87.30 257	87.94 294	98.12 161

ET-MVSNet_ETH3D		91.49 228	90.11 256	95.63 141	96.40 205	91.57 118	95.34 284	93.48 359	90.60 192	75.58 381	95.49 235	80.08 238	96.79 346	94.25 115	89.76 278	98.52 127

LFMVS		93.60 139	92.63 159	96.52 80	98.13 100	91.27 130	97.94 71	93.39 360	90.57 193	96.29 86	98.31 60	69.00 342	99.16 132	94.18 116	95.87 173	99.12 80

Patchmatch-RL test		87.38 316	86.24 319	90.81 331	88.74 384	78.40 374	88.12 389	93.17 361	87.11 294	82.17 361	89.29 372	81.95 208	95.60 364	88.64 230	77.02 372	98.41 142

test-LLR		91.42 231	91.19 213	92.12 300	94.59 303	80.66 351	94.29 322	92.98 362	91.11 169	90.76 222	92.37 342	79.02 259	98.07 249	88.81 226	96.74 157	97.63 187

test-mter		90.19 281	89.54 279	92.12 300	94.59 303	80.66 351	94.29 322	92.98 362	87.68 281	90.76 222	92.37 342	67.67 351	98.07 249	88.81 226	96.74 157	97.63 187

WB-MVSnew		89.88 288	89.56 278	90.82 330	94.57 306	83.06 330	95.65 272	92.85 364	87.86 272	90.83 221	94.10 300	79.66 247	96.88 343	76.34 359	94.19 204	92.54 363

testing3		87.67 314	86.88 315	90.05 342	96.14 220	80.71 350	97.10 167	92.85 364	90.15 201	87.54 306	94.55 273	55.70 385	94.10 377	73.77 372	94.10 208	95.35 282

test_method		66.11 364	64.89 366	69.79 381	72.62 404	35.23 412	65.19 399	92.83 366	20.35 402	65.20 391	88.08 381	43.14 393	82.70 399	73.12 375	63.46 394	91.45 377

test0.0.03 1		89.37 296	88.70 294	91.41 320	92.47 360	85.63 295	95.22 293	92.70 367	91.11 169	86.91 323	93.65 318	79.02 259	93.19 385	78.00 351	89.18 283	95.41 275

new_pmnet		82.89 346	81.12 351	88.18 355	89.63 378	80.18 360	91.77 368	92.57 368	76.79 383	75.56 382	88.23 379	61.22 377	94.48 373	71.43 379	82.92 352	89.87 383

mvsany_test1		93.93 128	93.98 111	93.78 245	94.94 284	86.80 272	94.62 305	92.55 369	88.77 247	96.85 60	98.49 38	88.98 88	98.08 245	95.03 96	95.62 180	96.46 230

thisisatest0515		92.29 196	91.30 207	95.25 160	96.60 186	88.90 218	94.36 317	92.32 370	87.92 269	93.43 157	94.57 272	77.28 284	99.00 155	89.42 210	95.86 174	97.86 176

thisisatest0530		93.03 165	92.21 176	95.49 151	97.07 154	89.11 214	97.49 130	92.19 371	90.16 200	94.09 141	96.41 186	76.43 292	99.05 151	90.38 190	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	92.09 372	90.63 188	93.88 147	97.01 148	76.50 289	99.06 150	90.29 193	95.45 183	98.38 145

K. test v3		87.64 315	86.75 317	90.32 339	93.02 350	79.48 368	96.61 211	92.08 373	90.66 186	80.25 370	94.09 301	67.21 355	96.65 348	85.96 281	80.83 360	94.83 313

TESTMET0.1,1		90.06 283	89.42 282	91.97 303	94.41 311	80.62 353	94.29 322	91.97 374	87.28 291	90.44 227	92.47 341	68.79 343	97.67 299	88.50 232	96.60 162	97.61 191

PM-MVS		83.48 343	81.86 349	88.31 353	87.83 387	77.59 376	93.43 348	91.75 375	86.91 296	80.63 366	89.91 368	44.42 392	95.84 358	85.17 292	76.73 375	91.50 375

baseline2		91.63 218	90.86 221	93.94 236	94.33 313	86.32 284	95.92 257	91.64 376	89.37 222	86.94 321	94.69 266	81.62 214	98.69 185	88.64 230	94.57 199	96.81 220

APD_test1		79.31 352	77.70 355	84.14 365	89.11 382	69.07 391	92.36 367	91.50 377	69.07 389	73.87 383	92.63 337	39.93 394	94.32 375	70.54 384	80.25 362	89.02 385

FPMVS		71.27 358	69.85 360	75.50 378	74.64 401	59.03 401	91.30 370	91.50 377	58.80 393	57.92 397	88.28 378	29.98 400	85.53 398	53.43 396	82.84 353	81.95 391

door								91.13 379

door-mid								91.06 380

EGC-MVSNET		68.77 362	63.01 367	86.07 364	92.49 359	82.24 339	93.96 331	90.96 381	0.71 407	2.62 408	90.89 360	53.66 386	93.46 382	57.25 394	84.55 334	82.51 390

mvsany_test3		83.59 342	82.44 346	87.03 360	83.80 392	73.82 383	93.70 340	90.92 382	86.42 304	82.51 359	90.26 364	46.76 391	95.71 360	90.82 183	76.76 374	91.57 373

pmmvs3		79.97 351	77.50 356	87.39 358	82.80 395	79.38 369	92.70 362	90.75 383	70.69 388	78.66 375	87.47 385	51.34 389	93.40 383	73.39 374	69.65 387	89.38 384

UWE-MVS		89.91 285	89.48 281	91.21 323	95.88 228	78.23 375	94.91 300	90.26 384	89.11 229	92.35 180	94.52 274	68.76 344	97.96 269	83.95 306	95.59 181	97.42 199

DSMNet-mixed		86.34 326	86.12 322	87.00 361	89.88 377	70.43 387	94.93 299	90.08 385	77.97 380	85.42 336	92.78 334	74.44 310	93.96 380	74.43 368	95.14 187	96.62 224

MVS-HIRNet		82.47 347	81.21 350	86.26 363	95.38 252	69.21 390	88.96 386	89.49 386	66.28 390	80.79 365	74.08 395	68.48 348	97.39 325	71.93 378	95.47 182	92.18 369

WB-MVS		76.77 354	76.63 357	77.18 373	85.32 390	56.82 403	94.53 309	89.39 387	82.66 354	71.35 385	89.18 373	75.03 305	88.88 393	35.42 401	66.79 391	85.84 387

test1111		93.19 155	92.82 150	94.30 216	97.58 137	84.56 314	98.21 43	89.02 388	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		76.05 355	75.83 358	76.72 377	84.77 391	56.22 404	94.32 320	88.96 389	81.82 360	70.52 386	88.91 374	74.79 307	88.71 394	33.69 402	64.71 393	85.23 388

ECVR-MVS		93.19 155	92.73 156	94.57 201	97.66 127	85.41 299	98.21 43	88.23 390	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		90.70 265	89.81 269	93.37 263	94.73 298	84.21 317	93.67 343	88.02 391	89.50 218	92.38 177	93.49 323	77.82 281	97.78 291	86.03 279	92.68 230	98.11 164

ANet_high		63.94 365	59.58 368	77.02 374	61.24 408	66.06 395	85.66 392	87.93 392	78.53 378	42.94 400	71.04 397	25.42 403	80.71 400	52.60 397	30.83 401	84.28 389

PMMVS2		70.19 359	66.92 362	80.01 369	76.35 400	65.67 396	86.22 390	87.58 393	64.83 392	62.38 393	80.29 392	26.78 402	88.49 396	63.79 388	54.07 398	85.88 386

lessismore_v0				90.45 337	91.96 366	79.09 372		87.19 394		80.32 369	94.39 282	66.31 362	97.55 310	84.00 305	76.84 373	94.70 325

PMVS	53.92 22	58.58 366	55.40 369	68.12 382	51.00 409	48.64 406	78.86 395	87.10 395	46.77 398	35.84 404	74.28 394	8.76 408	86.34 397	42.07 399	73.91 380	69.38 396
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_vis1_rt		86.16 329	85.06 330	89.46 348	93.47 341	80.46 355	96.41 223	86.61 396	85.22 323	79.15 374	88.64 375	52.41 388	97.06 335	93.08 139	90.57 268	90.87 379

testf1		69.31 360	66.76 363	76.94 375	78.61 398	61.93 399	88.27 387	86.11 397	55.62 394	59.69 394	85.31 387	20.19 406	89.32 390	57.62 392	69.44 388	79.58 392

APD_test2		69.31 360	66.76 363	76.94 375	78.61 398	61.93 399	88.27 387	86.11 397	55.62 394	59.69 394	85.31 387	20.19 406	89.32 390	57.62 392	69.44 388	79.58 392

gg-mvs-nofinetune		87.82 312	85.61 324	94.44 205	94.46 308	89.27 208	91.21 373	84.61 399	80.88 365	89.89 248	74.98 393	71.50 326	97.53 313	85.75 284	97.21 148	96.51 226

dmvs_testset		81.38 349	82.60 345	77.73 372	91.74 367	51.49 405	93.03 357	84.21 400	89.07 230	78.28 377	91.25 359	76.97 286	88.53 395	56.57 395	82.24 355	93.16 353

GG-mvs-BLEND				93.62 252	93.69 332	89.20 210	92.39 366	83.33 401		87.98 300	89.84 369	71.00 330	96.87 344	82.08 324	95.40 184	94.80 318

MTMP							97.86 79	82.03 402

DeepMVS_CX				74.68 380	90.84 372	64.34 398		81.61 403	65.34 391	67.47 389	88.01 382	48.60 390	80.13 401	62.33 390	73.68 381	79.58 392

E-PMN		53.28 367	52.56 371	55.43 384	74.43 402	47.13 407	83.63 394	76.30 404	42.23 399	42.59 401	62.22 400	28.57 401	74.40 402	31.53 403	31.51 400	44.78 399

test2506		91.60 220	90.78 226	94.04 227	97.66 127	83.81 322	98.27 33	75.53 405	93.43 89	95.23 119	98.21 67	67.21 355	99.07 148	93.01 144	98.49 105	99.25 68

EMVS		52.08 369	51.31 372	54.39 385	72.62 404	45.39 409	83.84 393	75.51 406	41.13 400	40.77 402	59.65 401	30.08 399	73.60 403	28.31 404	29.90 402	44.18 400

test_vis3_rt		72.73 356	70.55 359	79.27 370	80.02 397	68.13 393	93.92 334	74.30 407	76.90 382	58.99 396	73.58 396	20.29 405	95.37 368	84.16 301	72.80 383	74.31 395

MVE	50.73 23	53.25 368	48.81 373	66.58 383	65.34 407	57.50 402	72.49 397	70.94 408	40.15 401	39.28 403	63.51 399	6.89 410	73.48 404	38.29 400	42.38 399	68.76 397
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		51.94 370	53.82 370	46.29 386	33.73 410	45.30 410	78.32 396	67.24 409	18.02 403	50.93 399	87.05 386	52.99 387	53.11 405	70.76 382	25.29 403	40.46 401

N_pmnet		78.73 353	78.71 354	78.79 371	92.80 353	46.50 408	94.14 326	43.71 410	78.61 377	80.83 364	91.66 356	74.94 306	96.36 350	67.24 386	84.45 336	93.50 349

wuyk23d		25.11 371	24.57 375	26.74 387	73.98 403	39.89 411	57.88 400	9.80 411	12.27 404	10.39 405	6.97 407	7.03 409	36.44 406	25.43 405	17.39 404	3.89 404

testmvs		13.36 373	16.33 376	4.48 389	5.04 411	2.26 414	93.18 351	3.28 412	2.70 405	8.24 406	21.66 403	2.29 412	2.19 407	7.58 406	2.96 405	9.00 403

test123		13.04 374	15.66 377	5.18 388	4.51 412	3.45 413	92.50 365	1.81 413	2.50 406	7.58 407	20.15 404	3.67 411	2.18 408	7.13 407	1.07 406	9.90 402

test_blank		0.00 377	0.00 380	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	0.00 408	0.00 413	0.00 409	0.00 408	0.00 407	0.00 405

uanet_test		0.00 377	0.00 380	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	0.00 408	0.00 413	0.00 409	0.00 408	0.00 407	0.00 405

DCPMVS		0.00 377	0.00 380	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	0.00 408	0.00 413	0.00 409	0.00 408	0.00 407	0.00 405

pcd_1.5k_mvsjas		7.39 376	9.85 379	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	0.00 408	88.65 95	0.00 409	0.00 408	0.00 407	0.00 405

sosnet-low-res		0.00 377	0.00 380	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	0.00 408	0.00 413	0.00 409	0.00 408	0.00 407	0.00 405

sosnet		0.00 377	0.00 380	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	0.00 408	0.00 413	0.00 409	0.00 408	0.00 407	0.00 405

uncertanet		0.00 377	0.00 380	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	0.00 408	0.00 413	0.00 409	0.00 408	0.00 407	0.00 405

Regformer		0.00 377	0.00 380	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	0.00 408	0.00 413	0.00 409	0.00 408	0.00 407	0.00 405

n2								0.00 414

nn								0.00 414

ab-mvs-re		8.06 375	10.74 378	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	96.69 164	0.00 413	0.00 409	0.00 408	0.00 407	0.00 405

uanet		0.00 377	0.00 380	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	0.00 408	0.00 413	0.00 409	0.00 408	0.00 407	0.00 405

WAC-MVS						79.53 365								75.56 364

PC_three_1452									90.77 178	98.89 14	98.28 65	96.24 1	98.35 216	95.76 73	99.58 21	99.59 22

eth-test2					0.00 413

eth-test					0.00 413

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							92.81 361				16.58 406	80.53 229	97.68 298	86.20 273

test_post											17.58 405	81.76 211	98.08 245

patchmatchnet-post											90.45 363	82.65 194	98.10 241

gm-plane-assit					93.22 346	78.89 373			84.82 331		93.52 322		98.64 190	87.72 241

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		81.66 361	97.34 48			98.82 169	92.26 149

新几何2							95.79 264

原ACMM2							95.67 269

testdata2													99.67 56	85.96 281

segment_acmp												92.89 27

testdata1							95.26 292		93.10 105

plane_prior7					96.21 212	89.98 178

plane_prior6					96.10 223	90.00 174						81.32 217

plane_prior4											96.64 168

plane_prior3						90.00 174			94.46 54	91.34 207

plane_prior2							97.74 93		94.85 34

plane_prior1					96.14 220

plane_prior						89.99 176	97.24 153		94.06 65						92.16 239

HQP5-MVS						89.33 203

HQP-NCC					95.86 229		96.65 205		93.55 80	90.14 232

ACMP_Plane					95.86 229		96.65 205		93.55 80	90.14 232

BP-MVS														92.13 155

HQP4-MVS										90.14 232			98.50 202			95.78 255

HQP2-MVS												80.95 220

NP-MVS					95.99 227	89.81 183					95.87 210

MDTV_nov1_ep13_2view						70.35 388	93.10 356		83.88 342	93.55 152		82.47 198		86.25 272		98.38 145

ACMMP++_ref															90.30 273

ACMMP++															91.02 261

Test By Simon												88.73 94

High-resolution multi-view benchmark