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.

LCM-MVSNet		99.86 1	99.86 1	99.87 1	99.99 1	99.77 1	99.77 1	99.80 3	99.97 1	99.97 1	99.95 1	99.74 1	99.98 1	99.56 1	100.00 1	99.85 6

dcpmvs_2		97.12 138	97.99 63	94.51 314	99.11 92	84.00 373	97.75 82	99.65 13	97.38 86	99.14 40	98.42 125	95.16 159	99.96 2	95.52 154	99.78 58	99.58 43

mamv4		99.05 5	98.91 8	99.46 2	98.94 119	99.62 2	97.98 63	99.70 8	99.49 3	99.78 2	99.22 36	95.92 126	99.95 3	99.31 7	99.83 44	98.83 222

mvs_tets		98.90 6	98.94 6	98.75 35	99.69 10	96.48 64	98.54 23	99.22 39	96.23 129	99.71 5	99.48 12	98.77 7	99.93 4	98.89 21	99.95 5	99.84 8

DTE-MVSNet		98.79 9	98.86 9	98.59 50	99.55 22	96.12 76	98.48 30	99.10 60	99.36 5	99.29 32	99.06 58	97.27 49	99.93 4	97.71 60	99.91 17	99.70 29

UA-Net		98.88 8	98.76 14	99.22 3	99.11 92	97.89 17	99.47 3	99.32 31	99.08 14	97.87 170	99.67 3	96.47 105	99.92 6	97.88 49	99.98 2	99.85 6

PS-MVSNAJss		98.53 24	98.63 21	98.21 80	99.68 11	94.82 131	98.10 56	99.21 40	96.91 99	99.75 3	99.45 15	95.82 132	99.92 6	98.80 23	99.96 4	99.89 4

jajsoiax		98.77 10	98.79 13	98.74 38	99.66 12	96.48 64	98.45 31	99.12 56	95.83 159	99.67 8	99.37 21	98.25 14	99.92 6	98.77 24	99.94 8	99.82 9

PS-CasMVS		98.73 12	98.85 11	98.39 63	99.55 22	95.47 104	98.49 28	99.13 55	99.22 10	99.22 37	98.96 68	97.35 45	99.92 6	97.79 55	99.93 11	99.79 13

PEN-MVS		98.75 11	98.85 11	98.44 59	99.58 18	95.67 93	98.45 31	99.15 51	99.33 6	99.30 31	99.00 62	97.27 49	99.92 6	97.64 64	99.92 14	99.75 23

MVSFormer		96.14 193	96.36 185	95.49 268	97.68 281	87.81 316	98.67 15	99.02 86	96.50 116	94.48 327	96.15 318	86.90 310	99.92 6	98.73 26	99.13 229	98.74 235

test_djsdf		98.73 12	98.74 17	98.69 43	99.63 14	96.30 71	98.67 15	99.02 86	96.50 116	99.32 30	99.44 16	97.43 42	99.92 6	98.73 26	99.95 5	99.86 5

K. test v3		96.44 182	96.28 188	96.95 180	99.41 40	91.53 240	97.65 91	90.31 403	98.89 24	98.93 57	99.36 23	84.57 330	99.92 6	97.81 53	99.56 120	99.39 114

MVSMamba_PlusPlus		97.43 122	97.98 64	95.78 252	98.88 128	89.70 270	98.03 61	98.85 131	99.18 11	96.84 231	99.12 51	93.04 213	99.91 14	98.38 36	99.55 126	97.73 336

v7n		98.73 12	98.99 5	97.95 100	99.64 13	94.20 158	98.67 15	99.14 54	99.08 14	99.42 22	99.23 35	96.53 100	99.91 14	99.27 8	99.93 11	99.73 25

anonymousdsp		98.72 15	98.63 21	98.99 14	99.62 15	97.29 41	98.65 19	99.19 44	95.62 168	99.35 29	99.37 21	97.38 44	99.90 16	98.59 31	99.91 17	99.77 15

CP-MVSNet		98.42 30	98.46 30	98.30 70	99.46 34	95.22 120	98.27 44	98.84 135	99.05 17	99.01 49	98.65 101	95.37 151	99.90 16	97.57 65	99.91 17	99.77 15

HyFIR lowres test		93.72 297	92.65 314	96.91 185	98.93 121	91.81 236	91.23 386	98.52 196	82.69 395	96.46 257	96.52 301	80.38 355	99.90 16	90.36 321	98.79 268	99.03 187

WR-MVS_H		98.65 16	98.62 23	98.75 35	99.51 28	96.61 60	98.55 22	99.17 46	99.05 17	99.17 39	98.79 83	95.47 147	99.89 19	97.95 47	99.91 17	99.75 23

SixPastTwentyTwo		97.49 116	97.57 109	97.26 158	99.56 20	92.33 215	98.28 42	96.97 306	98.30 43	99.45 20	99.35 25	88.43 294	99.89 19	98.01 45	99.76 60	99.54 58

mvs5depth		98.06 53	98.58 26	96.51 212	98.97 115	89.65 272	99.43 4	99.81 2	99.30 7	98.36 110	99.86 2	93.15 210	99.88 21	98.50 34	99.84 40	99.99 1

TranMVSNet+NR-MVSNet		98.33 33	98.30 43	98.43 60	99.07 98	95.87 85	96.73 153	99.05 76	98.67 28	98.84 65	98.45 122	97.58 39	99.88 21	96.45 105	99.86 29	99.54 58

OurMVSNet-221017-0		98.61 17	98.61 25	98.63 48	99.77 5	96.35 68	99.17 7	99.05 76	98.05 54	99.61 14	99.52 9	93.72 200	99.88 21	98.72 28	99.88 24	99.65 36

patch_mono-2		96.59 174	96.93 151	95.55 265	98.88 128	87.12 329	94.47 291	99.30 33	94.12 229	96.65 245	98.41 127	94.98 166	99.87 24	95.81 140	99.78 58	99.66 33

SPE-MVS-test		97.91 74	97.84 76	98.14 84	98.52 177	96.03 81	98.38 34	99.67 10	98.11 51	95.50 303	96.92 276	96.81 88	99.87 24	96.87 93	99.76 60	98.51 260

UniMVSNet_ETH3D		99.12 3	99.28 3	98.65 46	99.77 5	96.34 69	99.18 6	99.20 42	99.67 2	99.73 4	99.65 6	99.15 3	99.86 26	97.22 75	99.92 14	99.77 15

CS-MVS		98.09 49	98.01 61	98.32 67	98.45 188	96.69 56	98.52 26	99.69 9	98.07 53	96.07 279	97.19 256	96.88 82	99.86 26	97.50 68	99.73 70	98.41 267

Vis-MVSNet		98.27 39	98.34 39	98.07 88	99.33 51	95.21 122	98.04 59	99.46 24	97.32 88	97.82 174	99.11 52	96.75 90	99.86 26	97.84 52	99.36 187	99.15 161
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		97.83 84	97.65 97	98.37 64	98.72 148	95.78 87	95.66 226	99.02 86	98.11 51	98.31 120	97.69 218	94.65 175	99.85 29	97.02 88	99.71 77	99.48 85

DU-MVS		97.79 90	97.60 106	98.36 65	98.73 145	95.78 87	95.65 228	98.87 124	97.57 72	98.31 120	97.83 202	94.69 171	99.85 29	97.02 88	99.71 77	99.46 90

EPP-MVSNet		96.84 156	96.58 171	97.65 121	99.18 78	93.78 174	98.68 14	96.34 320	97.91 57	97.30 195	98.06 181	88.46 293	99.85 29	93.85 241	99.40 181	99.32 126

LCM-MVSNet-Re		97.33 130	97.33 125	97.32 153	98.13 229	93.79 173	96.99 132	99.65 13	96.74 105	99.47 19	98.93 71	96.91 79	99.84 32	90.11 323	99.06 242	98.32 279

MIMVSNet1		98.51 25	98.45 33	98.67 44	99.72 8	96.71 54	98.76 13	98.89 115	98.49 35	99.38 25	99.14 50	95.44 149	99.84 32	96.47 104	99.80 52	99.47 88

reproduce_model		98.54 22	98.33 40	99.15 4	99.06 100	98.04 12	97.04 129	99.09 65	98.42 37	99.03 47	98.71 93	96.93 75	99.83 34	97.09 83	99.63 94	99.56 54

ANet_high		98.31 36	98.94 6	96.41 220	99.33 51	89.64 273	97.92 69	99.56 21	99.27 8	99.66 10	99.50 11	97.67 32	99.83 34	97.55 66	99.98 2	99.77 15

GDP-MVS		95.39 228	94.89 241	96.90 186	98.26 206	91.91 232	96.48 164	99.28 35	95.06 196	96.54 254	97.12 261	74.83 382	99.82 36	97.19 79	99.27 211	98.96 197

reproduce-ours		98.48 26	98.27 45	99.12 5	98.99 111	98.02 13	96.81 141	99.02 86	98.29 44	98.97 55	98.61 104	97.27 49	99.82 36	96.86 94	99.61 102	99.51 68

our_new_method		98.48 26	98.27 45	99.12 5	98.99 111	98.02 13	96.81 141	99.02 86	98.29 44	98.97 55	98.61 104	97.27 49	99.82 36	96.86 94	99.61 102	99.51 68

MTAPA		98.14 44	97.84 76	99.06 7	99.44 36	97.90 16	97.25 115	98.73 163	97.69 68	97.90 165	97.96 191	95.81 136	99.82 36	96.13 119	99.61 102	99.45 94

EC-MVSNet		97.90 76	97.94 68	97.79 109	98.66 157	95.14 123	98.31 39	99.66 12	97.57 72	95.95 283	97.01 270	96.99 70	99.82 36	97.66 63	99.64 92	98.39 270

MM		96.87 155	96.62 167	97.62 123	97.72 278	93.30 191	96.39 166	92.61 379	97.90 58	96.76 237	98.64 102	90.46 267	99.81 41	99.16 12	99.94 8	99.76 20

tttt0517		93.31 309	92.56 317	95.57 262	98.71 151	87.86 313	97.44 107	87.17 415	95.79 160	97.47 190	96.84 280	64.12 408	99.81 41	96.20 117	99.32 202	99.02 190

DPE-MVS		97.64 103	97.35 124	98.50 55	98.85 132	96.18 73	95.21 261	98.99 100	95.84 158	98.78 70	98.08 174	96.84 86	99.81 41	93.98 237	99.57 117	99.52 64
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
Effi-MVS+-dtu		96.81 161	96.09 196	98.99 14	96.90 335	98.69 5	96.42 165	98.09 251	95.86 157	95.15 310	95.54 340	94.26 186	99.81 41	94.06 232	98.51 295	98.47 264

MSP-MVS		97.45 119	96.92 153	99.03 9	99.26 57	97.70 22	97.66 90	98.89 115	95.65 166	98.51 91	96.46 303	92.15 240	99.81 41	95.14 184	98.58 290	99.58 43
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
FC-MVSNet-test		98.16 43	98.37 37	97.56 126	99.49 32	93.10 197	98.35 35	99.21 40	98.43 36	98.89 61	98.83 82	94.30 185	99.81 41	97.87 50	99.91 17	99.77 15

APDe-MVS		98.14 44	98.03 59	98.47 58	98.72 148	96.04 79	98.07 58	99.10 60	95.96 147	98.59 86	98.69 96	96.94 73	99.81 41	96.64 97	99.58 114	99.57 50
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
BP-MVS1		95.36 229	94.86 244	96.89 187	98.35 196	91.72 237	96.76 147	95.21 346	96.48 119	96.23 271	97.19 256	75.97 378	99.80 48	97.91 48	99.60 108	99.15 161

Anonymous20240521		97.07 140	97.51 115	95.76 253	99.35 49	88.18 304	97.78 78	98.40 211	97.11 94	98.34 114	99.04 59	89.58 280	99.79 49	98.09 42	99.93 11	99.30 131

ZNCC-MVS		97.92 71	97.62 104	98.83 29	99.32 53	97.24 43	97.45 106	98.84 135	95.76 161	96.93 226	97.43 235	97.26 53	99.79 49	96.06 120	99.53 134	99.45 94

RRT-MVS		95.78 208	96.25 189	94.35 320	96.68 338	84.47 367	97.72 86	99.11 57	97.23 91	97.27 197	98.72 90	86.39 314	99.79 49	95.49 155	97.67 337	98.80 226

HPM-MVS		98.11 48	97.83 79	98.92 25	99.42 39	97.46 35	98.57 20	99.05 76	95.43 180	97.41 193	97.50 231	97.98 20	99.79 49	95.58 153	99.57 117	99.50 71
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
h-mvs33		96.29 187	95.63 218	98.26 72	98.50 182	96.11 77	96.90 136	97.09 300	96.58 111	97.21 201	98.19 162	84.14 332	99.78 53	95.89 134	96.17 381	98.89 213

MVS_0304		95.71 211	95.18 227	97.33 152	94.85 393	92.82 201	95.36 247	90.89 396	95.51 174	95.61 299	97.82 205	88.39 295	99.78 53	98.23 39	99.91 17	99.40 109

FIs		97.93 70	98.07 54	97.48 139	99.38 46	92.95 200	98.03 61	99.11 57	98.04 55	98.62 82	98.66 98	93.75 199	99.78 53	97.23 74	99.84 40	99.73 25

MP-MVS		97.64 103	97.18 136	99.00 13	99.32 53	97.77 21	97.49 105	98.73 163	96.27 126	95.59 300	97.75 212	96.30 115	99.78 53	93.70 247	99.48 155	99.45 94
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PGM-MVS		97.88 78	97.52 114	98.96 17	99.20 75	97.62 25	97.09 126	99.06 72	95.45 177	97.55 181	97.94 194	97.11 58	99.78 53	94.77 205	99.46 160	99.48 85

UniMVSNet (Re)		97.83 84	97.65 97	98.35 66	98.80 136	95.86 86	95.92 210	99.04 83	97.51 76	98.22 128	97.81 207	94.68 173	99.78 53	97.14 81	99.75 68	99.41 108

NR-MVSNet		97.96 60	97.86 75	98.26 72	98.73 145	95.54 97	98.14 54	98.73 163	97.79 59	99.42 22	97.83 202	94.40 183	99.78 53	95.91 133	99.76 60	99.46 90

mPP-MVS		97.91 74	97.53 113	99.04 8	99.22 66	97.87 18	97.74 84	98.78 155	96.04 142	97.10 210	97.73 215	96.53 100	99.78 53	95.16 181	99.50 148	99.46 90

CP-MVS		97.92 71	97.56 110	98.99 14	98.99 111	97.82 19	97.93 68	98.96 107	96.11 135	96.89 229	97.45 233	96.85 85	99.78 53	95.19 177	99.63 94	99.38 116

PVSNet_Blended_VisFu		95.95 201	95.80 211	96.42 218	99.28 55	90.62 258	95.31 255	99.08 68	88.40 347	96.97 224	98.17 165	92.11 242	99.78 53	93.64 248	99.21 218	98.86 220

GeoE		97.75 93	97.70 90	97.89 103	98.88 128	94.53 142	97.10 125	98.98 103	95.75 163	97.62 179	97.59 224	97.61 38	99.77 63	96.34 111	99.44 164	99.36 122

SR-MVS		98.00 57	97.66 96	99.01 12	98.77 143	97.93 15	97.38 111	98.83 141	97.32 88	98.06 148	97.85 201	96.65 93	99.77 63	95.00 193	99.11 233	99.32 126

GST-MVS		97.82 87	97.49 118	98.81 31	99.23 63	97.25 42	97.16 120	98.79 151	95.96 147	97.53 182	97.40 237	96.93 75	99.77 63	95.04 190	99.35 192	99.42 106

thisisatest0530		92.71 320	91.76 329	95.56 264	98.42 191	88.23 302	96.03 197	87.35 414	94.04 233	96.56 251	95.47 342	64.03 409	99.77 63	94.78 204	99.11 233	98.68 245

MP-MVS-pluss		97.69 98	97.36 123	98.70 42	99.50 31	96.84 51	95.38 246	98.99 100	92.45 284	98.11 140	98.31 139	97.25 54	99.77 63	96.60 99	99.62 96	99.48 85
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SR-MVS-dyc-post		98.14 44	97.84 76	99.02 10	98.81 134	98.05 10	97.55 99	98.86 127	97.77 60	98.20 129	98.07 176	96.60 98	99.76 68	95.49 155	99.20 219	99.26 143

region2R		97.92 71	97.59 107	98.92 25	99.22 66	97.55 30	97.60 94	98.84 135	96.00 145	97.22 199	97.62 222	96.87 84	99.76 68	95.48 159	99.43 173	99.46 90

ACMMPR		97.95 64	97.62 104	98.94 19	99.20 75	97.56 29	97.59 96	98.83 141	96.05 140	97.46 191	97.63 221	96.77 89	99.76 68	95.61 150	99.46 160	99.49 79

SteuartSystems-ACMMP		98.02 56	97.76 87	98.79 33	99.43 37	97.21 45	97.15 121	98.90 114	96.58 111	98.08 145	97.87 200	97.02 68	99.76 68	95.25 174	99.59 111	99.40 109
Skip Steuart: Steuart Systems R&D Blog.
RPMNet		94.68 264	94.60 260	94.90 294	95.44 382	88.15 305	96.18 184	98.86 127	97.43 78	94.10 335	98.49 117	79.40 357	99.76 68	95.69 143	95.81 384	96.81 374

ACMMP		98.05 54	97.75 89	98.93 22	99.23 63	97.60 26	98.09 57	98.96 107	95.75 163	97.91 164	98.06 181	96.89 80	99.76 68	95.32 171	99.57 117	99.43 105
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
DVP-MVS++		97.96 60	97.90 69	98.12 86	97.75 273	95.40 105	99.03 8	98.89 115	96.62 107	98.62 82	98.30 143	96.97 71	99.75 74	95.70 141	99.25 214	99.21 151

MSC_two_6792asdad				98.22 77	97.75 273	95.34 112		98.16 244					99.75 74	95.87 136	99.51 144	99.57 50

No_MVS				98.22 77	97.75 273	95.34 112		98.16 244					99.75 74	95.87 136	99.51 144	99.57 50

test_0728_SECOND				98.25 75	99.23 63	95.49 103	96.74 149	98.89 115					99.75 74	95.48 159	99.52 139	99.53 61

IterMVS-SCA-FT		95.86 205	96.19 192	94.85 297	97.68 281	85.53 348	92.42 361	97.63 284	96.99 96	98.36 110	98.54 113	87.94 299	99.75 74	97.07 86	99.08 237	99.27 142

APD-MVS_3200maxsize		98.13 47	97.90 69	98.79 33	98.79 138	97.31 40	97.55 99	98.92 112	97.72 65	98.25 125	98.13 168	97.10 59	99.75 74	95.44 163	99.24 217	99.32 126

VPA-MVSNet		98.27 39	98.46 30	97.70 117	99.06 100	93.80 172	97.76 81	99.00 97	98.40 38	99.07 46	98.98 65	96.89 80	99.75 74	97.19 79	99.79 54	99.55 57

WR-MVS		96.90 152	96.81 158	97.16 163	98.56 172	92.20 222	94.33 294	98.12 249	97.34 87	98.20 129	97.33 248	92.81 219	99.75 74	94.79 202	99.81 49	99.54 58

QAPM		95.88 204	95.57 220	96.80 194	97.90 246	91.84 235	98.18 53	98.73 163	88.41 346	96.42 258	98.13 168	94.73 169	99.75 74	88.72 344	98.94 251	98.81 225

test_fmvsmconf0.01_n		98.57 18	98.74 17	98.06 90	99.39 44	94.63 138	96.70 155	99.82 1	95.44 179	99.64 11	99.52 9	98.96 4	99.74 83	99.38 5	99.86 29	99.81 10

ZD-MVS					98.43 190	95.94 83		98.56 194	90.72 314	96.66 243	97.07 264	95.02 164	99.74 83	91.08 294	98.93 253

HPM-MVS_fast		98.32 35	98.13 49	98.88 27	99.54 25	97.48 34	98.35 35	99.03 84	95.88 155	97.88 167	98.22 160	98.15 17	99.74 83	96.50 103	99.62 96	99.42 106

lessismore_v0				97.05 174	99.36 48	92.12 224		84.07 420		98.77 74	98.98 65	85.36 324	99.74 83	97.34 73	99.37 184	99.30 131

APD-MVS		97.00 143	96.53 177	98.41 61	98.55 173	96.31 70	96.32 174	98.77 156	92.96 274	97.44 192	97.58 226	95.84 129	99.74 83	91.96 276	99.35 192	99.19 155
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
IterMVS-LS		96.92 150	97.29 127	95.79 251	98.51 179	88.13 307	95.10 264	98.66 180	96.99 96	98.46 99	98.68 97	92.55 229	99.74 83	96.91 91	99.79 54	99.50 71
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mmtdpeth		98.33 33	98.53 28	97.71 115	99.07 98	93.44 186	98.80 12	99.78 4	99.10 13	96.61 247	99.63 7	95.42 150	99.73 89	98.53 33	99.86 29	99.95 2

test1111		94.53 272	94.81 249	93.72 335	99.06 100	81.94 388	98.31 39	83.87 421	96.37 122	98.49 94	99.17 46	81.49 347	99.73 89	96.64 97	99.86 29	99.49 79

GBi-Net		96.99 144	96.80 159	97.56 126	97.96 241	93.67 177	98.23 46	98.66 180	95.59 170	97.99 154	99.19 39	89.51 284	99.73 89	94.60 211	99.44 164	99.30 131

test1		96.99 144	96.80 159	97.56 126	97.96 241	93.67 177	98.23 46	98.66 180	95.59 170	97.99 154	99.19 39	89.51 284	99.73 89	94.60 211	99.44 164	99.30 131

FMVSNet1		97.95 64	98.08 53	97.56 126	99.14 90	93.67 177	98.23 46	98.66 180	97.41 83	99.00 51	99.19 39	95.47 147	99.73 89	95.83 138	99.76 60	99.30 131

3Dnovator	96.53 2	97.61 106	97.64 100	97.50 135	97.74 276	93.65 181	98.49 28	98.88 122	96.86 101	97.11 209	98.55 111	95.82 132	99.73 89	95.94 131	99.42 176	99.13 167

test_fmvsmconf0.1_n		98.41 31	98.54 27	98.03 95	99.16 80	94.61 139	96.18 184	99.73 5	95.05 197	99.60 15	99.34 26	98.68 8	99.72 95	99.21 10	99.85 38	99.76 20

SED-MVS		97.94 67	97.90 69	98.07 88	99.22 66	95.35 110	96.79 145	98.83 141	96.11 135	99.08 44	98.24 155	97.87 24	99.72 95	95.44 163	99.51 144	99.14 165

test_241102_TWO								98.83 141	96.11 135	98.62 82	98.24 155	96.92 78	99.72 95	95.44 163	99.49 151	99.49 79

SF-MVS		97.60 107	97.39 121	98.22 77	98.93 121	95.69 91	97.05 128	99.10 60	95.32 184	97.83 173	97.88 199	96.44 108	99.72 95	94.59 214	99.39 182	99.25 147

ETV-MVS		96.13 194	95.90 207	96.82 193	97.76 271	93.89 168	95.40 244	98.95 109	95.87 156	95.58 301	91.00 404	96.36 113	99.72 95	93.36 253	98.83 265	96.85 370

TSAR-MVS + MP.		97.42 123	97.23 132	98.00 97	99.38 46	95.00 127	97.63 93	98.20 234	93.00 269	98.16 135	98.06 181	95.89 127	99.72 95	95.67 145	99.10 235	99.28 138
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
xiu_mvs_v1_base_debu		95.62 216	95.96 203	94.60 308	98.01 235	88.42 297	93.99 313	98.21 231	92.98 270	95.91 285	94.53 359	96.39 110	99.72 95	95.43 166	98.19 310	95.64 395

ACMMP_NAP		97.89 77	97.63 102	98.67 44	99.35 49	96.84 51	96.36 171	98.79 151	95.07 195	97.88 167	98.35 134	97.24 55	99.72 95	96.05 122	99.58 114	99.45 94

xiu_mvs_v1_base		95.62 216	95.96 203	94.60 308	98.01 235	88.42 297	93.99 313	98.21 231	92.98 270	95.91 285	94.53 359	96.39 110	99.72 95	95.43 166	98.19 310	95.64 395

Anonymous20231211		98.55 21	98.76 14	97.94 101	98.79 138	94.37 150	98.84 11	99.15 51	99.37 4	99.67 8	99.43 17	95.61 143	99.72 95	98.12 40	99.86 29	99.73 25

xiu_mvs_v1_base_debi		95.62 216	95.96 203	94.60 308	98.01 235	88.42 297	93.99 313	98.21 231	92.98 270	95.91 285	94.53 359	96.39 110	99.72 95	95.43 166	98.19 310	95.64 395

XVS		97.96 60	97.63 102	98.94 19	99.15 83	97.66 23	97.77 79	98.83 141	97.42 79	96.32 263	97.64 220	96.49 103	99.72 95	95.66 146	99.37 184	99.45 94

X-MVStestdata		92.86 317	90.83 346	98.94 19	99.15 83	97.66 23	97.77 79	98.83 141	97.42 79	96.32 263	36.50 425	96.49 103	99.72 95	95.66 146	99.37 184	99.45 94

v10		97.55 112	97.97 65	96.31 226	98.60 166	89.64 273	97.44 107	99.02 86	96.60 109	98.72 79	99.16 47	93.48 204	99.72 95	98.76 25	99.92 14	99.58 43

test_fmvsmconf_n		98.30 37	98.41 36	97.99 98	98.94 119	94.60 140	96.00 200	99.64 16	94.99 200	99.43 21	99.18 43	98.51 10	99.71 109	99.13 13	99.84 40	99.67 31

DVP-MVS		97.78 91	97.65 97	98.16 81	99.24 61	95.51 99	96.74 149	98.23 230	95.92 152	98.40 104	98.28 148	97.06 64	99.71 109	95.48 159	99.52 139	99.26 143
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									96.62 107	98.40 104	98.28 148	97.10 59	99.71 109	95.70 141	99.62 96	99.58 43

CANet		95.86 205	95.65 217	96.49 214	96.41 345	90.82 254	94.36 293	98.41 209	94.94 201	92.62 379	96.73 289	92.68 223	99.71 109	95.12 187	99.60 108	98.94 201

xiu_mvs_v2_base		94.22 280	94.63 258	92.99 356	97.32 318	84.84 363	92.12 367	97.84 267	91.96 292	94.17 333	93.43 371	96.07 123	99.71 109	91.27 290	97.48 346	94.42 405

PS-MVSNAJ		94.10 286	94.47 268	93.00 355	97.35 313	84.88 360	91.86 372	97.84 267	91.96 292	94.17 333	92.50 389	95.82 132	99.71 109	91.27 290	97.48 346	94.40 406

v1240		96.74 164	97.02 146	95.91 247	98.18 217	88.52 296	95.39 245	98.88 122	93.15 265	98.46 99	98.40 130	92.80 220	99.71 109	98.45 35	99.49 151	99.49 79

IS-MVSNet		96.93 149	96.68 165	97.70 117	99.25 60	94.00 165	98.57 20	96.74 315	98.36 39	98.14 138	97.98 190	88.23 297	99.71 109	93.10 262	99.72 74	99.38 116

Fast-Effi-MVS+		95.49 221	95.07 232	96.75 198	97.67 285	92.82 201	94.22 301	98.60 188	91.61 299	93.42 360	92.90 380	96.73 91	99.70 117	92.60 267	97.89 324	97.74 335

v144192		96.69 170	96.90 155	96.03 239	98.25 207	88.92 288	95.49 237	98.77 156	93.05 267	98.09 143	98.29 147	92.51 234	99.70 117	98.11 41	99.56 120	99.47 88

v1921920		96.72 167	96.96 150	95.99 240	98.21 211	88.79 293	95.42 241	98.79 151	93.22 257	98.19 133	98.26 153	92.68 223	99.70 117	98.34 38	99.55 126	99.49 79

HFP-MVS		97.94 67	97.64 100	98.83 29	99.15 83	97.50 33	97.59 96	98.84 135	96.05 140	97.49 186	97.54 227	97.07 63	99.70 117	95.61 150	99.46 160	99.30 131

HPM-MVS++		96.99 144	96.38 184	98.81 31	98.64 158	97.59 27	95.97 204	98.20 234	95.51 174	95.06 312	96.53 299	94.10 189	99.70 117	94.29 223	99.15 226	99.13 167

LPG-MVS_test		97.94 67	97.67 95	98.74 38	99.15 83	97.02 46	97.09 126	99.02 86	95.15 191	98.34 114	98.23 157	97.91 22	99.70 117	94.41 217	99.73 70	99.50 71

LGP-MVS_train				98.74 38	99.15 83	97.02 46		99.02 86	95.15 191	98.34 114	98.23 157	97.91 22	99.70 117	94.41 217	99.73 70	99.50 71

test2506		89.86 360	89.16 365	91.97 378	98.95 116	76.83 415	98.54 23	61.07 430	96.20 130	97.07 216	99.16 47	55.19 424	99.69 124	96.43 106	99.83 44	99.38 116

tfpnnormal		97.72 96	97.97 65	96.94 181	99.26 57	92.23 218	97.83 76	98.45 202	98.25 46	99.13 41	98.66 98	96.65 93	99.69 124	93.92 239	99.62 96	98.91 209

Fast-Effi-MVS+-dtu		96.44 182	96.12 194	97.39 149	97.18 323	94.39 147	95.46 238	98.73 163	96.03 144	94.72 320	94.92 353	96.28 118	99.69 124	93.81 242	97.98 318	98.09 301

EI-MVSNet-UG-set		97.32 131	97.40 120	97.09 171	97.34 315	92.01 230	95.33 252	97.65 280	97.74 63	98.30 122	98.14 166	95.04 162	99.69 124	97.55 66	99.52 139	99.58 43

test_0402		97.84 83	97.97 65	97.47 140	99.19 77	94.07 161	96.71 154	98.73 163	98.66 29	98.56 88	98.41 127	96.84 86	99.69 124	94.82 200	99.81 49	98.64 246

fmvsm_l_conf0.5_n_3		98.29 38	98.46 30	97.79 109	98.90 126	94.05 163	96.06 194	99.63 17	96.07 138	99.37 26	98.93 71	98.29 13	99.68 129	99.11 14	99.79 54	99.65 36

SSC-MVS		95.92 202	97.03 145	92.58 367	99.28 55	78.39 404	96.68 156	95.12 348	98.90 23	99.11 42	98.66 98	91.36 255	99.68 129	95.00 193	99.16 225	99.67 31

balanced_conf03		96.88 154	97.29 127	95.63 259	97.66 286	89.47 277	97.95 66	98.89 115	95.94 150	97.77 177	98.55 111	92.23 238	99.68 129	97.05 87	99.61 102	97.73 336

SMA-MVS		97.48 117	97.11 138	98.60 49	98.83 133	96.67 57	96.74 149	98.73 163	91.61 299	98.48 96	98.36 133	96.53 100	99.68 129	95.17 179	99.54 130	99.45 94
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
pmmvs6		99.07 4	99.24 4	98.56 52	99.81 2	96.38 66	98.87 10	99.30 33	99.01 20	99.63 12	99.66 4	99.27 2	99.68 129	97.75 58	99.89 23	99.62 40

EI-MVSNet-Vis-set		97.32 131	97.39 121	97.11 167	97.36 312	92.08 228	95.34 251	97.65 280	97.74 63	98.29 123	98.11 172	95.05 161	99.68 129	97.50 68	99.50 148	99.56 54

v8		97.60 107	98.06 57	96.23 228	98.71 151	89.44 278	97.43 109	98.82 149	97.29 90	98.74 77	99.10 53	93.86 195	99.68 129	98.61 30	99.94 8	99.56 54

VPNet		97.26 133	97.49 118	96.59 206	99.47 33	90.58 259	96.27 176	98.53 195	97.77 60	98.46 99	98.41 127	94.59 176	99.68 129	94.61 210	99.29 208	99.52 64

mvsmamba		94.91 250	94.41 272	96.40 222	97.65 288	91.30 245	97.92 69	95.32 344	91.50 302	95.54 302	98.38 131	83.06 341	99.68 129	92.46 271	97.84 325	98.23 290

KD-MVS_self_test		97.86 82	98.07 54	97.25 159	99.22 66	92.81 203	97.55 99	98.94 110	97.10 95	98.85 64	98.88 79	95.03 163	99.67 138	97.39 72	99.65 90	99.26 143

EIA-MVS		96.04 197	95.77 213	96.85 190	97.80 261	92.98 199	96.12 190	99.16 47	94.65 210	93.77 346	91.69 398	95.68 140	99.67 138	94.18 227	98.85 262	97.91 321

v1192		96.83 159	97.06 143	96.15 236	98.28 202	89.29 280	95.36 247	98.77 156	93.73 239	98.11 140	98.34 136	93.02 217	99.67 138	98.35 37	99.58 114	99.50 71

CPTT-MVS		96.69 170	96.08 197	98.49 56	98.89 127	96.64 59	97.25 115	98.77 156	92.89 275	96.01 282	97.13 259	92.23 238	99.67 138	92.24 273	99.34 195	99.17 158

FMVSNet5		93.39 307	92.35 318	96.50 213	95.83 369	90.81 256	97.31 112	98.27 225	92.74 278	96.27 268	98.28 148	62.23 410	99.67 138	90.86 301	99.36 187	99.03 187

OpenMVS	94.22 8	95.48 223	95.20 225	96.32 225	97.16 324	91.96 231	97.74 84	98.84 135	87.26 357	94.36 329	98.01 187	93.95 194	99.67 138	90.70 312	98.75 272	97.35 356

ECVR-MVS		94.37 278	94.48 267	94.05 330	98.95 116	83.10 378	98.31 39	82.48 423	96.20 130	98.23 127	99.16 47	81.18 350	99.66 144	95.95 130	99.83 44	99.38 116

CSCG		97.40 124	97.30 126	97.69 119	98.95 116	94.83 130	97.28 114	98.99 100	96.35 125	98.13 139	95.95 329	95.99 124	99.66 144	94.36 222	99.73 70	98.59 252

fmvsm_s_conf0.5_n_3		97.88 78	98.37 37	96.41 220	98.73 145	89.82 268	95.94 208	99.49 23	96.81 102	99.09 43	99.03 60	97.09 61	99.65 146	99.37 6	99.76 60	99.76 20

fmvsm_l_conf0.5_n		97.68 100	97.81 81	97.27 156	98.92 123	92.71 208	95.89 212	99.41 30	93.36 251	99.00 51	98.44 124	96.46 107	99.65 146	99.09 15	99.76 60	99.45 94

v1144		96.84 156	97.08 141	96.13 237	98.42 191	89.28 281	95.41 243	98.67 178	94.21 224	97.97 158	98.31 139	93.06 212	99.65 146	98.06 44	99.62 96	99.45 94

jason		94.39 277	94.04 284	95.41 273	98.29 200	87.85 315	92.74 350	96.75 314	85.38 380	95.29 307	96.15 318	88.21 298	99.65 146	94.24 225	99.34 195	98.74 235
jason: jason.
FMVSNet2		96.72 167	96.67 166	96.87 189	97.96 241	91.88 233	97.15 121	98.06 257	95.59 170	98.50 93	98.62 103	89.51 284	99.65 146	94.99 195	99.60 108	99.07 182

fmvsm_l_conf0.5_n_a		97.60 107	97.76 87	97.11 167	98.92 123	92.28 216	95.83 215	99.32 31	93.22 257	98.91 60	98.49 117	96.31 114	99.64 151	99.07 16	99.76 60	99.40 109

test_fmvsm_n_1920		98.08 50	98.29 44	97.43 144	98.88 128	93.95 167	96.17 188	99.57 19	95.66 165	99.52 16	98.71 93	97.04 66	99.64 151	99.21 10	99.87 27	98.69 242

EPNet		93.72 297	92.62 316	97.03 177	87.61 428	92.25 217	96.27 176	91.28 392	96.74 105	87.65 414	97.39 241	85.00 326	99.64 151	92.14 274	99.48 155	99.20 154
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
1112_ss		94.12 285	93.42 296	96.23 228	98.59 168	90.85 253	94.24 299	98.85 131	85.49 376	92.97 368	94.94 351	86.01 317	99.64 151	91.78 283	97.92 321	98.20 294

v2v482		96.78 163	97.06 143	95.95 244	98.57 170	88.77 294	95.36 247	98.26 226	95.18 190	97.85 172	98.23 157	92.58 227	99.63 155	97.80 54	99.69 81	99.45 94

lupinMVS		93.77 295	93.28 298	95.24 276	97.68 281	87.81 316	92.12 367	96.05 323	84.52 389	94.48 327	95.06 349	86.90 310	99.63 155	93.62 249	99.13 229	98.27 287

FMVSNet3		95.26 236	94.94 236	96.22 230	96.53 342	90.06 263	95.99 202	97.66 278	94.11 230	97.99 154	97.91 198	80.22 356	99.63 155	94.60 211	99.44 164	98.96 197

ACMP	92.54 13	97.47 118	97.10 139	98.55 53	99.04 107	96.70 55	96.24 181	98.89 115	93.71 240	97.97 158	97.75 212	97.44 41	99.63 155	93.22 259	99.70 80	99.32 126
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LS3D		97.77 92	97.50 117	98.57 51	96.24 348	97.58 28	98.45 31	98.85 131	98.58 32	97.51 184	97.94 194	95.74 139	99.63 155	95.19 177	98.97 247	98.51 260

SDMVSNet		97.97 58	98.26 47	97.11 167	99.41 40	92.21 219	96.92 135	98.60 188	98.58 32	98.78 70	99.39 18	97.80 26	99.62 160	94.98 196	99.86 29	99.52 64

9.14			96.69 164		98.53 176		96.02 198	98.98 103	93.23 256	97.18 204	97.46 232	96.47 105	99.62 160	92.99 263	99.32 202

VDDNet		96.98 147	96.84 156	97.41 147	99.40 43	93.26 194	97.94 67	95.31 345	99.26 9	98.39 106	99.18 43	87.85 304	99.62 160	95.13 186	99.09 236	99.35 124

V42		97.04 141	97.16 137	96.68 203	98.59 168	91.05 249	96.33 173	98.36 216	94.60 212	97.99 154	98.30 143	93.32 206	99.62 160	97.40 71	99.53 134	99.38 116

DeepC-MVS	95.41 4	97.82 87	97.70 90	98.16 81	98.78 141	95.72 89	96.23 182	99.02 86	93.92 236	98.62 82	98.99 64	97.69 30	99.62 160	96.18 118	99.87 27	99.15 161
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+	96.13 3	97.73 94	97.59 107	98.15 83	98.11 230	95.60 95	98.04 59	98.70 172	98.13 50	96.93 226	98.45 122	95.30 154	99.62 160	95.64 148	98.96 248	99.24 148

ACMM	93.33 11	98.05 54	97.79 83	98.85 28	99.15 83	97.55 30	96.68 156	98.83 141	95.21 187	98.36 110	98.13 168	98.13 19	99.62 160	96.04 123	99.54 130	99.39 114
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20240529		97.96 60	98.04 58	97.71 115	98.69 155	94.28 156	97.86 73	98.31 224	98.79 26	99.23 36	98.86 81	95.76 138	99.61 167	95.49 155	99.36 187	99.23 149

nrg030		98.54 22	98.62 23	98.32 67	99.22 66	95.66 94	97.90 71	99.08 68	98.31 41	99.02 48	98.74 89	97.68 31	99.61 167	97.77 57	99.85 38	99.70 29

fmvsm_s_conf0.1_n_2		97.68 100	98.18 48	96.20 231	99.06 100	89.08 287	95.51 236	99.72 6	96.06 139	99.48 17	99.24 33	95.18 157	99.60 169	99.45 2	99.88 24	99.94 3

test_fmvsmvis_n_1920		98.08 50	98.47 29	96.93 182	99.03 108	93.29 192	96.32 174	99.65 13	95.59 170	99.71 5	99.01 61	97.66 34	99.60 169	99.44 3	99.83 44	97.90 322

fmvsm_s_conf0.5_n_2		97.59 110	98.07 54	96.17 234	98.78 141	89.10 286	95.33 252	99.55 22	95.96 147	99.41 24	99.10 53	95.18 157	99.59 171	99.43 4	99.86 29	99.81 10

IB-MVS	85.98 20	88.63 372	86.95 383	93.68 337	95.12 390	84.82 364	90.85 392	90.17 405	87.55 356	88.48 411	91.34 401	58.01 413	99.59 171	87.24 367	93.80 404	96.63 380
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
TDRefinement		98.90 6	98.86 9	99.02 10	99.54 25	98.06 9	99.34 5	99.44 26	98.85 25	99.00 51	99.20 38	97.42 43	99.59 171	97.21 76	99.76 60	99.40 109

thisisatest0515		90.43 352	89.18 364	94.17 328	97.07 328	85.44 349	89.75 406	87.58 413	88.28 349	93.69 350	91.72 397	65.27 407	99.58 174	90.59 314	98.67 280	97.50 351

VDD-MVS		97.37 127	97.25 130	97.74 113	98.69 155	94.50 145	97.04 129	95.61 337	98.59 31	98.51 91	98.72 90	92.54 231	99.58 174	96.02 125	99.49 151	99.12 172

EI-MVSNet		96.63 173	96.93 151	95.74 254	97.26 320	88.13 307	95.29 257	97.65 280	96.99 96	97.94 162	98.19 162	92.55 229	99.58 174	96.91 91	99.56 120	99.50 71

DELS-MVS		96.17 192	96.23 190	95.99 240	97.55 298	90.04 264	92.38 364	98.52 196	94.13 228	96.55 253	97.06 265	94.99 165	99.58 174	95.62 149	99.28 209	98.37 272
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
MVSTER		94.21 282	93.93 289	95.05 285	95.83 369	86.46 338	95.18 262	97.65 280	92.41 285	97.94 162	98.00 189	72.39 394	99.58 174	96.36 109	99.56 120	99.12 172

IterMVS		95.42 227	95.83 210	94.20 326	97.52 299	83.78 375	92.41 362	97.47 289	95.49 176	98.06 148	98.49 117	87.94 299	99.58 174	96.02 125	99.02 244	99.23 149
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CANet_DTU		94.65 266	94.21 278	95.96 242	95.90 364	89.68 271	93.92 318	97.83 269	93.19 260	90.12 400	95.64 337	88.52 292	99.57 180	93.27 258	99.47 157	98.62 249

sd_testset		97.97 58	98.12 50	97.51 131	99.41 40	93.44 186	97.96 64	98.25 227	98.58 32	98.78 70	99.39 18	98.21 15	99.56 181	92.65 266	99.86 29	99.52 64

Effi-MVS+		96.19 191	96.01 199	96.71 200	97.43 308	92.19 223	96.12 190	99.10 60	95.45 177	93.33 362	94.71 356	97.23 56	99.56 181	93.21 260	97.54 343	98.37 272

XVG-ACMP-BASELINE		97.58 111	97.28 129	98.49 56	99.16 80	96.90 50	96.39 166	98.98 103	95.05 197	98.06 148	98.02 185	95.86 128	99.56 181	94.37 220	99.64 92	99.00 191

Test_1112_low_res		93.53 304	92.86 306	95.54 266	98.60 166	88.86 291	92.75 348	98.69 173	82.66 396	92.65 376	96.92 276	84.75 328	99.56 181	90.94 299	97.76 329	98.19 295

AUN-MVS		93.95 294	92.69 313	97.74 113	97.80 261	95.38 107	95.57 235	95.46 341	91.26 308	92.64 377	96.10 323	74.67 383	99.55 185	93.72 246	96.97 356	98.30 283

TransMVSNet (Re)		98.38 32	98.67 19	97.51 131	99.51 28	93.39 190	98.20 51	98.87 124	98.23 47	99.48 17	99.27 31	98.47 11	99.55 185	96.52 102	99.53 134	99.60 41

Baseline_NR-MVSNet		97.72 96	97.79 83	97.50 135	99.56 20	93.29 192	95.44 239	98.86 127	98.20 49	98.37 107	99.24 33	94.69 171	99.55 185	95.98 129	99.79 54	99.65 36

hse-mvs2		95.77 209	95.09 231	97.79 109	97.84 253	95.51 99	95.66 226	95.43 342	96.58 111	97.21 201	96.16 317	84.14 332	99.54 188	95.89 134	96.92 357	98.32 279

VNet		96.84 156	96.83 157	96.88 188	98.06 231	92.02 229	96.35 172	97.57 286	97.70 67	97.88 167	97.80 208	92.40 236	99.54 188	94.73 207	98.96 248	99.08 180

Anonymous202405211		96.34 186	95.98 202	97.43 144	98.25 207	93.85 170	96.74 149	94.41 357	97.72 65	98.37 107	98.03 184	87.15 309	99.53 190	94.06 232	99.07 239	98.92 208

agg_prior					97.80 261	94.96 128		98.36 216		93.49 356			99.53 190

UGNet		96.81 161	96.56 173	97.58 125	96.64 339	93.84 171	97.75 82	97.12 299	96.47 120	93.62 351	98.88 79	93.22 209	99.53 190	95.61 150	99.69 81	99.36 122
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
TEST9					97.84 253	95.23 117	93.62 327	98.39 212	86.81 364	93.78 344	95.99 325	94.68 173	99.52 193

train_agg		95.46 225	94.66 254	97.88 104	97.84 253	95.23 117	93.62 327	98.39 212	87.04 360	93.78 344	95.99 325	94.58 177	99.52 193	91.76 284	98.90 255	98.89 213

test_8					97.81 257	95.07 126	93.54 330	98.38 214	87.04 360	93.71 348	95.96 328	94.58 177	99.52 193

LTVRE_ROB	96.88 1	99.18 2	99.34 2	98.72 41	99.71 9	96.99 48	99.69 2	99.57 19	99.02 19	99.62 13	99.36 23	98.53 9	99.52 193	98.58 32	99.95 5	99.66 33
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
new-patchmatchnet		95.67 214	96.58 171	92.94 358	97.48 302	80.21 399	92.96 343	98.19 239	94.83 204	98.82 67	98.79 83	93.31 207	99.51 197	95.83 138	99.04 243	99.12 172

WB-MVS		95.50 220	96.62 167	92.11 377	99.21 73	77.26 414	96.12 190	95.40 343	98.62 30	98.84 65	98.26 153	91.08 258	99.50 198	93.37 252	98.70 278	99.58 43

FE-MVS		92.95 316	92.22 321	95.11 281	97.21 322	88.33 301	98.54 23	93.66 365	89.91 327	96.21 273	98.14 166	70.33 401	99.50 198	87.79 355	98.24 309	97.51 349

EGC-MVSNET		83.08 389	77.93 392	98.53 54	99.57 19	97.55 30	98.33 38	98.57 193	4.71 427	10.38 428	98.90 77	95.60 144	99.50 198	95.69 143	99.61 102	98.55 256

pm-mvs1		98.47 28	98.67 19	97.86 105	99.52 27	94.58 141	98.28 42	99.00 97	97.57 72	99.27 33	99.22 36	98.32 12	99.50 198	97.09 83	99.75 68	99.50 71

casdiffmvs_mvg		97.83 84	98.11 51	97.00 179	98.57 170	92.10 227	95.97 204	99.18 45	97.67 71	99.00 51	98.48 121	97.64 35	99.50 198	96.96 90	99.54 130	99.40 109
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
thres600view7		92.03 334	91.43 332	93.82 332	98.19 214	84.61 365	96.27 176	90.39 400	96.81 102	96.37 261	93.11 373	73.44 392	99.49 203	80.32 404	97.95 320	97.36 354

ab-mvs		96.59 174	96.59 170	96.60 205	98.64 158	92.21 219	98.35 35	97.67 276	94.45 218	96.99 221	98.79 83	94.96 167	99.49 203	90.39 320	99.07 239	98.08 302

DP-MVS		97.87 80	97.89 72	97.81 108	98.62 164	94.82 131	97.13 124	98.79 151	98.98 21	98.74 77	98.49 117	95.80 137	99.49 203	95.04 190	99.44 164	99.11 175

LFMVS		95.32 233	94.88 243	96.62 204	98.03 232	91.47 242	97.65 91	90.72 399	99.11 12	97.89 166	98.31 139	79.20 358	99.48 206	93.91 240	99.12 232	98.93 205

Vis-MVSNet (Re-imp)		95.11 242	94.85 245	95.87 249	99.12 91	89.17 282	97.54 104	94.92 352	96.50 116	96.58 249	97.27 251	83.64 337	99.48 206	88.42 349	99.67 87	98.97 196

CHOSEN 280x420		89.98 357	89.19 363	92.37 372	95.60 379	81.13 395	86.22 414	97.09 300	81.44 401	87.44 415	93.15 372	73.99 384	99.47 208	88.69 345	99.07 239	96.52 382

CDS-MVSNet		94.88 253	94.12 282	97.14 165	97.64 291	93.57 182	93.96 317	97.06 302	90.05 325	96.30 267	96.55 297	86.10 316	99.47 208	90.10 324	99.31 205	98.40 268
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMH	93.61 9	98.44 29	98.76 14	97.51 131	99.43 37	93.54 183	98.23 46	99.05 76	97.40 84	99.37 26	99.08 57	98.79 6	99.47 208	97.74 59	99.71 77	99.50 71
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
WBMVS		91.11 346	90.72 348	92.26 374	95.99 361	77.98 409	91.47 378	95.90 329	91.63 297	95.90 288	96.45 304	59.60 411	99.46 211	89.97 327	99.59 111	99.33 125

testdata2													99.46 211	87.84 354

MDA-MVSNet-bldmvs		95.69 212	95.67 215	95.74 254	98.48 185	88.76 295	92.84 345	97.25 292	96.00 145	97.59 180	97.95 193	91.38 254	99.46 211	93.16 261	96.35 376	98.99 194

HQP_MVS		96.66 172	96.33 187	97.68 120	98.70 153	94.29 153	96.50 162	98.75 160	96.36 123	96.16 276	96.77 286	91.91 250	99.46 211	92.59 268	99.20 219	99.28 138

plane_prior5								98.75 160					99.46 211	92.59 268	99.20 219	99.28 138

新几何1				97.25 159	98.29 200	94.70 135		97.73 273	77.98 413	94.83 319	96.67 292	92.08 244	99.45 216	88.17 353	98.65 284	97.61 344

NCCC		96.52 178	95.99 201	98.10 87	97.81 257	95.68 92	95.00 273	98.20 234	95.39 181	95.40 306	96.36 310	93.81 197	99.45 216	93.55 250	98.42 301	99.17 158

COLMAP_ROB	94.48 6	98.25 41	98.11 51	98.64 47	99.21 73	97.35 39	97.96 64	99.16 47	98.34 40	98.78 70	98.52 114	97.32 46	99.45 216	94.08 231	99.67 87	99.13 167
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ET-MVSNet_ETH3D		91.12 345	89.67 358	95.47 269	96.41 345	89.15 284	91.54 377	90.23 404	89.07 336	86.78 418	92.84 382	69.39 403	99.44 219	94.16 228	96.61 371	97.82 328

CDPH-MVS		95.45 226	94.65 255	97.84 107	98.28 202	94.96 128	93.73 325	98.33 220	85.03 383	95.44 304	96.60 295	95.31 153	99.44 219	90.01 325	99.13 229	99.11 175

testing3		89.72 362	88.26 371	94.10 329	97.66 286	84.30 371	94.80 279	88.25 412	94.66 209	95.07 311	92.51 388	41.15 430	99.43 221	91.81 282	98.44 300	98.55 256

MCST-MVS		96.24 189	95.80 211	97.56 126	98.75 144	94.13 160	94.66 286	98.17 240	90.17 324	96.21 273	96.10 323	95.14 160	99.43 221	94.13 230	98.85 262	99.13 167

thres100view900		91.76 339	91.26 339	93.26 344	98.21 211	84.50 366	96.39 166	90.39 400	96.87 100	96.33 262	93.08 377	73.44 392	99.42 223	78.85 409	97.74 330	95.85 391

tfpn200view9		91.55 341	91.00 341	93.21 348	98.02 233	84.35 369	95.70 221	90.79 397	96.26 127	95.90 288	92.13 393	73.62 389	99.42 223	78.85 409	97.74 330	95.85 391

patchmatchnet-post											96.84 280	77.36 369	99.42 223

SCA		93.38 308	93.52 295	92.96 357	96.24 348	81.40 392	93.24 339	94.00 360	91.58 301	94.57 323	96.97 271	87.94 299	99.42 223	89.47 334	97.66 339	98.06 308

thres400		91.68 340	91.00 341	93.71 336	98.02 233	84.35 369	95.70 221	90.79 397	96.26 127	95.90 288	92.13 393	73.62 389	99.42 223	78.85 409	97.74 330	97.36 354

test12				97.46 141	97.61 293	94.07 161		97.78 271		93.57 354		93.31 207	99.42 223		98.78 269	98.89 213

CHOSEN 1792x2688		94.10 286	93.41 297	96.18 233	99.16 80	90.04 264	92.15 366	98.68 175	79.90 407	96.22 272	97.83 202	87.92 303	99.42 223	89.18 338	99.65 90	99.08 180

TAMVS		95.49 221	94.94 236	97.16 163	98.31 198	93.41 189	95.07 268	96.82 311	91.09 310	97.51 184	97.82 205	89.96 276	99.42 223	88.42 349	99.44 164	98.64 246

PHI-MVS		96.96 148	96.53 177	98.25 75	97.48 302	96.50 63	96.76 147	98.85 131	93.52 246	96.19 275	96.85 279	95.94 125	99.42 223	93.79 243	99.43 173	98.83 222

ADS-MVSNet2		91.47 343	90.51 352	94.36 319	95.51 380	85.63 346	95.05 270	95.70 332	83.46 393	92.69 374	96.84 280	79.15 359	99.41 232	85.66 377	90.52 411	98.04 312

XXY-MVS		97.54 113	97.70 90	97.07 173	99.46 34	92.21 219	97.22 118	99.00 97	94.93 203	98.58 87	98.92 73	97.31 47	99.41 232	94.44 215	99.43 173	99.59 42

alignmvs		96.01 199	95.52 221	97.50 135	97.77 270	94.71 133	96.07 193	96.84 309	97.48 77	96.78 236	94.28 365	85.50 323	99.40 234	96.22 116	98.73 276	98.40 268

无先验							93.20 340	97.91 261	80.78 403				99.40 234	87.71 356		97.94 320

HY-MVS	91.43 15	92.58 321	91.81 327	94.90 294	96.49 343	88.87 290	97.31 112	94.62 354	85.92 372	90.50 395	96.84 280	85.05 325	99.40 234	83.77 393	95.78 387	96.43 384

ACMH+	93.58 10	98.23 42	98.31 41	97.98 99	99.39 44	95.22 120	97.55 99	99.20 42	98.21 48	99.25 35	98.51 116	98.21 15	99.40 234	94.79 202	99.72 74	99.32 126

OPM-MVS		97.54 113	97.25 130	98.41 61	99.11 92	96.61 60	95.24 259	98.46 201	94.58 215	98.10 142	98.07 176	97.09 61	99.39 238	95.16 181	99.44 164	99.21 151
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v148		96.58 176	96.97 148	95.42 271	98.63 162	87.57 320	95.09 265	97.90 262	95.91 154	98.24 126	97.96 191	93.42 205	99.39 238	96.04 123	99.52 139	99.29 137

CR-MVSNet		93.29 311	92.79 309	94.78 302	95.44 382	88.15 305	96.18 184	97.20 294	84.94 386	94.10 335	98.57 108	77.67 365	99.39 238	95.17 179	95.81 384	96.81 374

fmvsm_s_conf0.1_n		97.73 94	98.02 60	96.85 190	99.09 95	91.43 244	96.37 170	99.11 57	94.19 226	99.01 49	99.25 32	96.30 115	99.38 241	99.00 18	99.88 24	99.73 25

fmvsm_s_conf0.5_n		97.62 105	97.89 72	96.80 194	98.79 138	91.44 243	96.14 189	99.06 72	94.19 226	98.82 67	98.98 65	96.22 120	99.38 241	98.98 20	99.86 29	99.58 43

原ACMM1				96.58 207	98.16 222	92.12 224		98.15 246	85.90 373	93.49 356	96.43 305	92.47 235	99.38 241	87.66 358	98.62 286	98.23 290

mvs_anonymous		95.36 229	96.07 198	93.21 348	96.29 347	81.56 390	94.60 288	97.66 278	93.30 254	96.95 225	98.91 76	93.03 216	99.38 241	96.60 99	97.30 354	98.69 242

Patchmtry		95.03 247	94.59 262	96.33 224	94.83 395	90.82 254	96.38 169	97.20 294	96.59 110	97.49 186	98.57 108	77.67 365	99.38 241	92.95 265	99.62 96	98.80 226

fmvsm_s_conf0.1_n_a		97.80 89	98.01 61	97.18 162	99.17 79	92.51 211	96.57 159	99.15 51	93.68 243	98.89 61	99.30 29	96.42 109	99.37 246	99.03 17	99.83 44	99.66 33

casdiffmvs		97.50 115	97.81 81	96.56 210	98.51 179	91.04 250	95.83 215	99.09 65	97.23 91	98.33 117	98.30 143	97.03 67	99.37 246	96.58 101	99.38 183	99.28 138
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
114514_t		93.96 292	93.22 300	96.19 232	99.06 100	90.97 252	95.99 202	98.94 110	73.88 420	93.43 359	96.93 274	92.38 237	99.37 246	89.09 339	99.28 209	98.25 289

fmvsm_s_conf0.5_n_a		97.65 102	97.83 79	97.13 166	98.80 136	92.51 211	96.25 180	99.06 72	93.67 244	98.64 80	99.00 62	96.23 119	99.36 249	98.99 19	99.80 52	99.53 61

ppachtmachnet_test		94.49 274	94.84 246	93.46 341	96.16 354	82.10 385	90.59 395	97.48 288	90.53 318	97.01 220	97.59 224	91.01 259	99.36 249	93.97 238	99.18 223	98.94 201

baseline		97.44 120	97.78 86	96.43 217	98.52 177	90.75 257	96.84 138	99.03 84	96.51 115	97.86 171	98.02 185	96.67 92	99.36 249	97.09 83	99.47 157	99.19 155

CNVR-MVS		96.92 150	96.55 174	98.03 95	98.00 239	95.54 97	94.87 277	98.17 240	94.60 212	96.38 260	97.05 266	95.67 141	99.36 249	95.12 187	99.08 237	99.19 155

MGCFI-Net		97.20 136	97.23 132	97.08 172	97.68 281	93.71 176	97.79 77	99.09 65	97.40 84	96.59 248	93.96 367	97.67 32	99.35 253	96.43 106	98.50 296	98.17 298

eth_miper_zixun_eth		94.89 252	94.93 238	94.75 303	95.99 361	86.12 343	91.35 381	98.49 199	93.40 249	97.12 208	97.25 253	86.87 312	99.35 253	95.08 189	98.82 266	98.78 229

F-COLMAP		95.30 234	94.38 273	98.05 94	98.64 158	96.04 79	95.61 232	98.66 180	89.00 338	93.22 363	96.40 308	92.90 218	99.35 253	87.45 364	97.53 344	98.77 232

Anonymous20231206		95.27 235	95.06 234	95.88 248	98.72 148	89.37 279	95.70 221	97.85 265	88.00 353	96.98 223	97.62 222	91.95 247	99.34 256	89.21 337	99.53 134	98.94 201

test_prior				97.46 141	97.79 266	94.26 157		98.42 208					99.34 256			98.79 228

sasdasda		97.23 134	97.21 134	97.30 154	97.65 288	94.39 147	97.84 74	99.05 76	97.42 79	96.68 240	93.85 369	97.63 36	99.33 258	96.29 112	98.47 297	98.18 296

test_241102_ONE					99.22 66	95.35 110		98.83 141	96.04 142	99.08 44	98.13 168	97.87 24	99.33 258

canonicalmvs		97.23 134	97.21 134	97.30 154	97.65 288	94.39 147	97.84 74	99.05 76	97.42 79	96.68 240	93.85 369	97.63 36	99.33 258	96.29 112	98.47 297	98.18 296

baseline2		89.65 364	88.44 370	93.25 345	95.62 378	82.71 380	93.82 321	85.94 418	88.89 340	87.35 416	92.54 387	71.23 397	99.33 258	86.01 372	94.60 400	97.72 338

WTY-MVS		93.55 303	93.00 304	95.19 278	97.81 257	87.86 313	93.89 319	96.00 325	89.02 337	94.07 337	95.44 344	86.27 315	99.33 258	87.69 357	96.82 363	98.39 270

DIV-MVS_self_test		94.73 257	94.64 256	95.01 287	95.86 367	87.00 331	91.33 382	98.08 252	93.34 252	97.10 210	97.34 247	84.02 335	99.31 263	95.15 183	99.55 126	98.72 238

thres200		91.00 349	90.42 353	92.77 363	97.47 306	83.98 374	94.01 312	91.18 394	95.12 193	95.44 304	91.21 402	73.93 385	99.31 263	77.76 412	97.63 341	95.01 402

PCF-MVS	89.43 18	92.12 330	90.64 350	96.57 209	97.80 261	93.48 185	89.88 405	98.45 202	74.46 419	96.04 281	95.68 335	90.71 264	99.31 263	73.73 417	99.01 246	96.91 367
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
cl____		94.73 257	94.64 256	95.01 287	95.85 368	87.00 331	91.33 382	98.08 252	93.34 252	97.10 210	97.33 248	84.01 336	99.30 266	95.14 184	99.56 120	98.71 241

tpm		91.08 348	90.85 345	91.75 380	95.33 386	78.09 406	95.03 272	91.27 393	88.75 341	93.53 355	97.40 237	71.24 396	99.30 266	91.25 292	93.87 403	97.87 325

PVSNet_BlendedMVS		95.02 248	94.93 238	95.27 275	97.79 266	87.40 324	94.14 307	98.68 175	88.94 339	94.51 325	98.01 187	93.04 213	99.30 266	89.77 330	99.49 151	99.11 175

PVSNet_Blended		93.96 292	93.65 292	94.91 292	97.79 266	87.40 324	91.43 379	98.68 175	84.50 390	94.51 325	94.48 362	93.04 213	99.30 266	89.77 330	98.61 287	98.02 314

diffmvs		96.04 197	96.23 190	95.46 270	97.35 313	88.03 310	93.42 333	99.08 68	94.09 232	96.66 243	96.93 274	93.85 196	99.29 270	96.01 127	98.67 280	99.06 184
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EG-PatchMatch MVS		97.69 98	97.79 83	97.40 148	99.06 100	93.52 184	95.96 206	98.97 106	94.55 216	98.82 67	98.76 88	97.31 47	99.29 270	97.20 78	99.44 164	99.38 116

FA-MVS(test-final)		94.91 250	94.89 241	94.99 289	97.51 300	88.11 309	98.27 44	95.20 347	92.40 286	96.68 240	98.60 106	83.44 338	99.28 272	93.34 254	98.53 291	97.59 346

c3_l		95.20 238	95.32 222	94.83 299	96.19 352	86.43 340	91.83 373	98.35 219	93.47 248	97.36 194	97.26 252	88.69 290	99.28 272	95.41 169	99.36 187	98.78 229

DeepC-MVS_fast	94.34 7	96.74 164	96.51 179	97.44 143	97.69 280	94.15 159	96.02 198	98.43 205	93.17 264	97.30 195	97.38 243	95.48 146	99.28 272	93.74 244	99.34 195	98.88 217
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
pmmvs5		94.63 267	94.34 274	95.50 267	97.63 292	88.34 300	94.02 311	97.13 298	87.15 359	95.22 309	97.15 258	87.50 305	99.27 275	93.99 236	99.26 213	98.88 217

miper_lstm_enhance		94.81 256	94.80 250	94.85 297	96.16 354	86.45 339	91.14 388	98.20 234	93.49 247	97.03 218	97.37 245	84.97 327	99.26 276	95.28 172	99.56 120	98.83 222

MVS_Test		96.27 188	96.79 161	94.73 304	96.94 333	86.63 337	96.18 184	98.33 220	94.94 201	96.07 279	98.28 148	95.25 155	99.26 276	97.21 76	97.90 323	98.30 283

UWE-MVS		87.57 382	86.72 384	90.13 393	95.21 387	73.56 423	91.94 371	83.78 422	88.73 343	93.00 367	92.87 381	55.22 423	99.25 278	81.74 398	97.96 319	97.59 346

testf1		98.57 18	98.45 33	98.93 22	99.79 3	98.78 3	97.69 87	99.42 28	97.69 68	98.92 58	98.77 86	97.80 26	99.25 278	96.27 114	99.69 81	98.76 233

APD_test2		98.57 18	98.45 33	98.93 22	99.79 3	98.78 3	97.69 87	99.42 28	97.69 68	98.92 58	98.77 86	97.80 26	99.25 278	96.27 114	99.69 81	98.76 233

OpenMVS_ROB	91.80 14	93.64 301	93.05 301	95.42 271	97.31 319	91.21 248	95.08 267	96.68 318	81.56 399	96.88 230	96.41 306	90.44 269	99.25 278	85.39 381	97.67 337	95.80 393

PatchT		93.75 296	93.57 294	94.29 324	95.05 391	87.32 326	96.05 195	92.98 372	97.54 75	94.25 330	98.72 90	75.79 379	99.24 282	95.92 132	95.81 384	96.32 385

RPSCF		97.87 80	97.51 115	98.95 18	99.15 83	98.43 7	97.56 98	99.06 72	96.19 132	98.48 96	98.70 95	94.72 170	99.24 282	94.37 220	99.33 200	99.17 158

HQP4-MVS										92.87 369			99.23 284			99.06 184

HQP-MVS		95.17 241	94.58 263	96.92 183	97.85 248	92.47 213	94.26 295	98.43 205	93.18 261	92.86 370	95.08 347	90.33 270	99.23 284	90.51 317	98.74 273	99.05 186

testing91		89.67 363	88.55 368	93.04 352	95.90 364	81.80 389	92.71 352	93.71 361	93.71 240	90.18 399	90.15 410	57.11 415	99.22 286	87.17 368	96.32 377	98.12 300

miper_ehance_all_eth		94.69 262	94.70 253	94.64 305	95.77 374	86.22 342	91.32 384	98.24 229	91.67 296	97.05 217	96.65 293	88.39 295	99.22 286	94.88 197	98.34 304	98.49 263

PLC	91.02 16	94.05 289	92.90 305	97.51 131	98.00 239	95.12 125	94.25 298	98.25 227	86.17 369	91.48 389	95.25 345	91.01 259	99.19 288	85.02 385	96.69 369	98.22 292
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_yl		94.40 275	94.00 285	95.59 260	96.95 331	89.52 275	94.75 283	95.55 339	96.18 133	96.79 232	96.14 320	81.09 351	99.18 289	90.75 307	97.77 327	98.07 304

DCV-MVSNet		94.40 275	94.00 285	95.59 260	96.95 331	89.52 275	94.75 283	95.55 339	96.18 133	96.79 232	96.14 320	81.09 351	99.18 289	90.75 307	97.77 327	98.07 304

YYNet1		94.73 257	94.84 246	94.41 318	97.47 306	85.09 358	90.29 398	95.85 331	92.52 281	97.53 182	97.76 209	91.97 246	99.18 289	93.31 256	96.86 360	98.95 199

PatchmatchNet		91.98 335	91.87 325	92.30 373	94.60 398	79.71 400	95.12 263	93.59 367	89.52 331	93.61 352	97.02 268	77.94 363	99.18 289	90.84 302	94.57 401	98.01 315
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDA-MVSNet_test_wron		94.73 257	94.83 248	94.42 317	97.48 302	85.15 356	90.28 399	95.87 330	92.52 281	97.48 188	97.76 209	91.92 249	99.17 293	93.32 255	96.80 365	98.94 201

CL-MVSNet_self_test		95.04 245	94.79 251	95.82 250	97.51 300	89.79 269	91.14 388	96.82 311	93.05 267	96.72 238	96.40 308	90.82 262	99.16 294	91.95 277	98.66 282	98.50 262

UnsupCasMVSNet_bld		94.72 261	94.26 275	96.08 238	98.62 164	90.54 262	93.38 335	98.05 258	90.30 321	97.02 219	96.80 285	89.54 281	99.16 294	88.44 348	96.18 380	98.56 254

testing99		89.21 367	88.04 373	92.70 365	95.78 373	81.00 396	92.65 353	92.03 382	93.20 259	89.90 403	90.08 412	55.25 422	99.14 296	87.54 361	95.95 383	97.97 317

APD_test1		97.95 64	97.68 94	98.75 35	99.60 16	98.60 6	97.21 119	99.08 68	96.57 114	98.07 147	98.38 131	96.22 120	99.14 296	94.71 209	99.31 205	98.52 259

miper_enhance_ethall		93.14 314	92.78 311	94.20 326	93.65 411	85.29 353	89.97 401	97.85 265	85.05 382	96.15 278	94.56 358	85.74 319	99.14 296	93.74 244	98.34 304	98.17 298

D2MVS		95.18 239	95.17 228	95.21 277	97.76 271	87.76 318	94.15 305	97.94 260	89.77 329	96.99 221	97.68 219	87.45 306	99.14 296	95.03 192	99.81 49	98.74 235

AllTest		97.20 136	96.92 153	98.06 90	99.08 96	96.16 74	97.14 123	99.16 47	94.35 221	97.78 175	98.07 176	95.84 129	99.12 300	91.41 287	99.42 176	98.91 209

TestCases				98.06 90	99.08 96	96.16 74		99.16 47	94.35 221	97.78 175	98.07 176	95.84 129	99.12 300	91.41 287	99.42 176	98.91 209

MAR-MVS		94.21 282	93.03 302	97.76 112	96.94 333	97.44 37	96.97 133	97.15 297	87.89 355	92.00 384	92.73 385	92.14 241	99.12 300	83.92 390	97.51 345	96.73 377
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
testing11		88.93 369	87.63 377	92.80 362	95.87 366	81.49 391	92.48 357	91.54 388	91.62 298	88.27 412	90.24 408	55.12 425	99.11 303	87.30 366	96.28 379	97.81 330

our_test_3		94.20 284	94.58 263	93.07 351	96.16 354	81.20 394	90.42 397	96.84 309	90.72 314	97.14 206	97.13 259	90.47 266	99.11 303	94.04 235	98.25 308	98.91 209

EPNet_dtu		91.39 344	90.75 347	93.31 343	90.48 424	82.61 382	94.80 279	92.88 373	93.39 250	81.74 422	94.90 354	81.36 349	99.11 303	88.28 351	98.87 259	98.21 293
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVP-Stereo		95.69 212	95.28 223	96.92 183	98.15 224	93.03 198	95.64 231	98.20 234	90.39 320	96.63 246	97.73 215	91.63 252	99.10 306	91.84 281	97.31 353	98.63 248
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
AdaColmap		95.11 242	94.62 259	96.58 207	97.33 317	94.45 146	94.92 275	98.08 252	93.15 265	93.98 342	95.53 341	94.34 184	99.10 306	85.69 376	98.61 287	96.20 388

pmmvs-eth3d		96.49 179	96.18 193	97.42 146	98.25 207	94.29 153	94.77 282	98.07 256	89.81 328	97.97 158	98.33 137	93.11 211	99.08 308	95.46 162	99.84 40	98.89 213

test_post											10.87 428	76.83 372	99.07 309

N_pmnet		95.18 239	94.23 276	98.06 90	97.85 248	96.55 62	92.49 356	91.63 387	89.34 332	98.09 143	97.41 236	90.33 270	99.06 310	91.58 286	99.31 205	98.56 254

reproduce_monomvs		92.05 333	92.26 320	91.43 383	95.42 384	75.72 419	95.68 224	97.05 303	94.47 217	97.95 161	98.35 134	55.58 421	99.05 311	96.36 109	99.44 164	99.51 68

PM-MVS		97.36 129	97.10 139	98.14 84	98.91 125	96.77 53	96.20 183	98.63 186	93.82 237	98.54 89	98.33 137	93.98 192	99.05 311	95.99 128	99.45 163	98.61 251

ambc				96.56 210	98.23 210	91.68 239	97.88 72	98.13 248		98.42 102	98.56 110	94.22 187	99.04 313	94.05 234	99.35 192	98.95 199

test_post1							94.98 274				10.37 429	76.21 376	99.04 313	89.47 334

OMC-MVS		96.48 180	96.00 200	97.91 102	98.30 199	96.01 82	94.86 278	98.60 188	91.88 294	97.18 204	97.21 255	96.11 122	99.04 313	90.49 319	99.34 195	98.69 242

MIMVSNet		93.42 306	92.86 306	95.10 283	98.17 220	88.19 303	98.13 55	93.69 362	92.07 288	95.04 315	98.21 161	80.95 353	99.03 316	81.42 400	98.06 316	98.07 304

DPM-MVS		93.68 299	92.77 312	96.42 218	97.91 245	92.54 209	91.17 387	97.47 289	84.99 385	93.08 366	94.74 355	89.90 277	99.00 317	87.54 361	98.09 315	97.72 338

BH-RMVSNet		94.56 270	94.44 271	94.91 292	97.57 295	87.44 323	93.78 324	96.26 321	93.69 242	96.41 259	96.50 302	92.10 243	99.00 317	85.96 373	97.71 333	98.31 281

gm-plane-assit					91.79 421	71.40 427			81.67 398		90.11 411		98.99 319	84.86 386

MVS_111021_HR		96.73 166	96.54 176	97.27 156	98.35 196	93.66 180	93.42 333	98.36 216	94.74 206	96.58 249	96.76 288	96.54 99	98.99 319	94.87 198	99.27 211	99.15 161

testdata				95.70 257	98.16 222	90.58 259		97.72 274	80.38 405	95.62 298	97.02 268	92.06 245	98.98 321	89.06 341	98.52 292	97.54 348

DP-MVS Recon		95.55 219	95.13 229	96.80 194	98.51 179	93.99 166	94.60 288	98.69 173	90.20 323	95.78 293	96.21 316	92.73 222	98.98 321	90.58 315	98.86 261	97.42 353

TAPA-MVS	93.32 12	94.93 249	94.23 276	97.04 176	98.18 217	94.51 143	95.22 260	98.73 163	81.22 402	96.25 270	95.95 329	93.80 198	98.98 321	89.89 328	98.87 259	97.62 343
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CLD-MVS		95.47 224	95.07 232	96.69 202	98.27 204	92.53 210	91.36 380	98.67 178	91.22 309	95.78 293	94.12 366	95.65 142	98.98 321	90.81 303	99.72 74	98.57 253
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
GA-MVS		92.83 318	92.15 323	94.87 296	96.97 330	87.27 327	90.03 400	96.12 322	91.83 295	94.05 338	94.57 357	76.01 377	98.97 325	92.46 271	97.34 352	98.36 277

BH-untuned		94.69 262	94.75 252	94.52 313	97.95 244	87.53 321	94.07 310	97.01 304	93.99 234	97.10 210	95.65 336	92.65 225	98.95 326	87.60 359	96.74 366	97.09 360

UBG		88.29 375	87.17 379	91.63 381	96.08 359	78.21 405	91.61 375	91.50 389	89.67 330	89.71 404	88.97 414	59.01 412	98.91 327	81.28 401	96.72 368	97.77 333

JIA-IIPM		91.79 338	90.69 349	95.11 281	93.80 410	90.98 251	94.16 304	91.78 386	96.38 121	90.30 398	99.30 29	72.02 395	98.90 328	88.28 351	90.17 413	95.45 399

pmmvs4		94.82 255	94.19 279	96.70 201	97.42 309	92.75 207	92.09 369	96.76 313	86.80 365	95.73 296	97.22 254	89.28 287	98.89 329	93.28 257	99.14 227	98.46 266

TSAR-MVS + GP.		96.47 181	96.12 194	97.49 138	97.74 276	95.23 117	94.15 305	96.90 308	93.26 255	98.04 151	96.70 290	94.41 182	98.89 329	94.77 205	99.14 227	98.37 272

CostFormer		89.75 361	89.25 359	91.26 386	94.69 397	78.00 408	95.32 254	91.98 384	81.50 400	90.55 394	96.96 273	71.06 398	98.89 329	88.59 347	92.63 407	96.87 368

sss		94.22 280	93.72 291	95.74 254	97.71 279	89.95 266	93.84 320	96.98 305	88.38 348	93.75 347	95.74 333	87.94 299	98.89 329	91.02 296	98.10 314	98.37 272

tpmvs		90.79 351	90.87 344	90.57 390	92.75 419	76.30 416	95.79 218	93.64 366	91.04 311	91.91 385	96.26 313	77.19 371	98.86 333	89.38 336	89.85 414	96.56 381

tpmrst		90.31 353	90.61 351	89.41 395	94.06 407	72.37 426	95.06 269	93.69 362	88.01 352	92.32 382	96.86 278	77.45 367	98.82 334	91.04 295	87.01 418	97.04 362

Gipuma		98.07 52	98.31 41	97.36 150	99.76 7	96.28 72	98.51 27	99.10 60	98.76 27	96.79 232	99.34 26	96.61 96	98.82 334	96.38 108	99.50 148	96.98 363
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Patchmatch-RL test		94.66 265	94.49 266	95.19 278	98.54 175	88.91 289	92.57 354	98.74 162	91.46 304	98.32 118	97.75 212	77.31 370	98.81 336	96.06 120	99.61 102	97.85 326

dp		88.08 377	88.05 372	88.16 402	92.85 417	68.81 428	94.17 303	92.88 373	85.47 377	91.38 390	96.14 320	68.87 404	98.81 336	86.88 369	83.80 421	96.87 368

DeepPCF-MVS	94.58 5	96.90 152	96.43 182	98.31 69	97.48 302	97.23 44	92.56 355	98.60 188	92.84 276	98.54 89	97.40 237	96.64 95	98.78 338	94.40 219	99.41 180	98.93 205

cl22		93.25 312	92.84 308	94.46 316	94.30 401	86.00 344	91.09 390	96.64 319	90.74 313	95.79 291	96.31 312	78.24 362	98.77 339	94.15 229	98.34 304	98.62 249

MG-MVS		94.08 288	94.00 285	94.32 322	97.09 327	85.89 345	93.19 341	95.96 327	92.52 281	94.93 318	97.51 230	89.54 281	98.77 339	87.52 363	97.71 333	98.31 281

EU-MVSNet		94.25 279	94.47 268	93.60 338	98.14 226	82.60 383	97.24 117	92.72 376	85.08 381	98.48 96	98.94 70	82.59 345	98.76 341	97.47 70	99.53 134	99.44 104

USDC		94.56 270	94.57 265	94.55 312	97.78 269	86.43 340	92.75 348	98.65 185	85.96 371	96.91 228	97.93 196	90.82 262	98.74 342	90.71 311	99.59 111	98.47 264

test_vis1_n_1920		95.77 209	96.41 183	93.85 331	98.55 173	84.86 362	95.91 211	99.71 7	92.72 279	97.67 178	98.90 77	87.44 307	98.73 343	97.96 46	98.85 262	97.96 318

tpm2		88.47 373	87.69 376	90.79 388	94.98 392	77.34 412	95.09 265	91.83 385	77.51 416	89.40 406	96.41 306	67.83 405	98.73 343	83.58 395	92.60 408	96.29 386

MVS_111021_LR		96.82 160	96.55 174	97.62 123	98.27 204	95.34 112	93.81 323	98.33 220	94.59 214	96.56 251	96.63 294	96.61 96	98.73 343	94.80 201	99.34 195	98.78 229

test20.03		96.58 176	96.61 169	96.48 215	98.49 183	91.72 237	95.68 224	97.69 275	96.81 102	98.27 124	97.92 197	94.18 188	98.71 346	90.78 305	99.66 89	99.00 191

testing222		87.35 383	85.50 390	92.93 359	95.79 372	82.83 379	92.40 363	90.10 406	92.80 277	88.87 409	89.02 413	48.34 428	98.70 347	75.40 415	96.74 366	97.27 358

ADS-MVSNet		90.95 350	90.26 354	93.04 352	95.51 380	82.37 384	95.05 270	93.41 368	83.46 393	92.69 374	96.84 280	79.15 359	98.70 347	85.66 377	90.52 411	98.04 312

pmmvs3		90.00 356	88.90 366	93.32 342	94.20 405	85.34 350	91.25 385	92.56 380	78.59 411	93.82 343	95.17 346	67.36 406	98.69 349	89.08 340	98.03 317	95.92 389

UnsupCasMVSNet_eth		95.91 203	95.73 214	96.44 216	98.48 185	91.52 241	95.31 255	98.45 202	95.76 161	97.48 188	97.54 227	89.53 283	98.69 349	94.43 216	94.61 399	99.13 167

LF4IMVS		96.07 195	95.63 218	97.36 150	98.19 214	95.55 96	95.44 239	98.82 149	92.29 287	95.70 297	96.55 297	92.63 226	98.69 349	91.75 285	99.33 200	97.85 326

TinyColmap		96.00 200	96.34 186	94.96 291	97.90 246	87.91 312	94.13 308	98.49 199	94.41 219	98.16 135	97.76 209	96.29 117	98.68 352	90.52 316	99.42 176	98.30 283

旧先验2							93.35 336		77.95 414	95.77 295			98.67 353	90.74 310

PMMVS		92.39 323	91.08 340	96.30 227	93.12 415	92.81 203	90.58 396	95.96 327	79.17 410	91.85 386	92.27 390	90.29 274	98.66 354	89.85 329	96.68 370	97.43 352

ETVMVS		87.62 381	85.75 388	93.22 347	96.15 357	83.26 377	92.94 344	90.37 402	91.39 305	90.37 396	88.45 415	51.93 427	98.64 355	73.76 416	96.38 375	97.75 334

KD-MVS_2432*1600		88.93 369	87.74 374	92.49 368	88.04 426	81.99 386	89.63 407	95.62 335	91.35 306	95.06 312	93.11 373	56.58 417	98.63 356	85.19 382	95.07 393	96.85 370

miper_refine_blended		88.93 369	87.74 374	92.49 368	88.04 426	81.99 386	89.63 407	95.62 335	91.35 306	95.06 312	93.11 373	56.58 417	98.63 356	85.19 382	95.07 393	96.85 370

Patchmatch-test		93.60 302	93.25 299	94.63 306	96.14 358	87.47 322	96.04 196	94.50 356	93.57 245	96.47 256	96.97 271	76.50 373	98.61 358	90.67 313	98.41 302	97.81 330

TR-MVS		92.54 322	92.20 322	93.57 339	96.49 343	86.66 336	93.51 331	94.73 353	89.96 326	94.95 316	93.87 368	90.24 275	98.61 358	81.18 402	94.88 396	95.45 399

baseline1		93.14 314	92.64 315	94.62 307	97.34 315	87.20 328	96.67 158	93.02 371	94.71 208	96.51 255	95.83 332	81.64 346	98.60 360	90.00 326	88.06 417	98.07 304

test-LLR		89.97 358	89.90 356	90.16 391	94.24 403	74.98 420	89.89 402	89.06 408	92.02 290	89.97 401	90.77 406	73.92 386	98.57 361	91.88 279	97.36 350	96.92 365

test-mter		87.92 379	87.17 379	90.16 391	94.24 403	74.98 420	89.89 402	89.06 408	86.44 368	89.97 401	90.77 406	54.96 426	98.57 361	91.88 279	97.36 350	96.92 365

PatchMatch-RL		94.61 268	93.81 290	97.02 178	98.19 214	95.72 89	93.66 326	97.23 293	88.17 351	94.94 317	95.62 338	91.43 253	98.57 361	87.36 365	97.68 336	96.76 376

DSMNet-mixed		92.19 328	91.83 326	93.25 345	96.18 353	83.68 376	96.27 176	93.68 364	76.97 417	92.54 380	99.18 43	89.20 289	98.55 364	83.88 391	98.60 289	97.51 349

MDTV_nov1_ep13			91.28 336		94.31 400	73.51 424	94.80 279	93.16 370	86.75 366	93.45 358	97.40 237	76.37 374	98.55 364	88.85 342	96.43 373

ITE_SJBPF				97.85 106	98.64 158	96.66 58		98.51 198	95.63 167	97.22 199	97.30 250	95.52 145	98.55 364	90.97 298	98.90 255	98.34 278

OPU-MVS				97.64 122	98.01 235	95.27 115	96.79 145				97.35 246	96.97 71	98.51 367	91.21 293	99.25 214	99.14 165

Syy-MVS		92.09 331	91.80 328	92.93 359	95.19 388	82.65 381	92.46 358	91.35 390	90.67 316	91.76 387	87.61 417	85.64 322	98.50 368	94.73 207	96.84 361	97.65 341

myMVS_eth3d		87.16 386	85.61 389	91.82 379	95.19 388	79.32 401	92.46 358	91.35 390	90.67 316	91.76 387	87.61 417	41.96 429	98.50 368	82.66 396	96.84 361	97.65 341

tt0805		97.44 120	97.56 110	97.11 167	99.55 22	96.36 67	98.66 18	95.66 333	98.31 41	97.09 215	95.45 343	97.17 57	98.50 368	98.67 29	97.45 349	96.48 383

PVSNet	86.72 19	91.10 347	90.97 343	91.49 382	97.56 297	78.04 407	87.17 412	94.60 355	84.65 388	92.34 381	92.20 392	87.37 308	98.47 371	85.17 384	97.69 335	97.96 318

CVMVSNet		92.33 326	92.79 309	90.95 387	97.26 320	75.84 418	95.29 257	92.33 381	81.86 397	96.27 268	98.19 162	81.44 348	98.46 372	94.23 226	98.29 307	98.55 256

XVG-OURS-SEG-HR		97.38 125	97.07 142	98.30 70	99.01 110	97.41 38	94.66 286	99.02 86	95.20 188	98.15 137	97.52 229	98.83 5	98.43 373	94.87 198	96.41 374	99.07 182

XVG-OURS		97.12 138	96.74 162	98.26 72	98.99 111	97.45 36	93.82 321	99.05 76	95.19 189	98.32 118	97.70 217	95.22 156	98.41 374	94.27 224	98.13 313	98.93 205

PAPM		87.64 380	85.84 387	93.04 352	96.54 341	84.99 359	88.42 411	95.57 338	79.52 408	83.82 419	93.05 379	80.57 354	98.41 374	62.29 423	92.79 406	95.71 394

MVS		90.02 355	89.20 362	92.47 370	94.71 396	86.90 333	95.86 213	96.74 315	64.72 422	90.62 392	92.77 383	92.54 231	98.39 376	79.30 407	95.56 391	92.12 414

PAPM_NR		94.61 268	94.17 280	95.96 242	98.36 195	91.23 247	95.93 209	97.95 259	92.98 270	93.42 360	94.43 363	90.53 265	98.38 377	87.60 359	96.29 378	98.27 287

MSDG		95.33 232	95.13 229	95.94 246	97.40 310	91.85 234	91.02 391	98.37 215	95.30 185	96.31 266	95.99 325	94.51 180	98.38 377	89.59 332	97.65 340	97.60 345

API-MVS		95.09 244	95.01 235	95.31 274	96.61 340	94.02 164	96.83 139	97.18 296	95.60 169	95.79 291	94.33 364	94.54 179	98.37 379	85.70 375	98.52 292	93.52 410

CNLPA		95.04 245	94.47 268	96.75 198	97.81 257	95.25 116	94.12 309	97.89 263	94.41 219	94.57 323	95.69 334	90.30 273	98.35 380	86.72 371	98.76 271	96.64 378

PAPR		92.22 327	91.27 337	95.07 284	95.73 377	88.81 292	91.97 370	97.87 264	85.80 374	90.91 391	92.73 385	91.16 256	98.33 381	79.48 406	95.76 388	98.08 302

test_cas_vis1_n_1920		95.34 231	95.67 215	94.35 320	98.21 211	86.83 335	95.61 232	99.26 37	90.45 319	98.17 134	98.96 68	84.43 331	98.31 382	96.74 96	99.17 224	97.90 322

tpm cat1		88.01 378	87.33 378	90.05 394	94.48 399	76.28 417	94.47 291	94.35 358	73.84 421	89.26 407	95.61 339	73.64 388	98.30 383	84.13 389	86.20 419	95.57 398

WB-MVSnew		91.50 342	91.29 335	92.14 376	94.85 393	80.32 398	93.29 338	88.77 410	88.57 345	94.03 339	92.21 391	92.56 228	98.28 384	80.21 405	97.08 355	97.81 330

BH-w/o		92.14 329	91.94 324	92.73 364	97.13 326	85.30 352	92.46 358	95.64 334	89.33 333	94.21 331	92.74 384	89.60 279	98.24 385	81.68 399	94.66 398	94.66 404

gg-mvs-nofinetune		88.28 376	86.96 382	92.23 375	92.84 418	84.44 368	98.19 52	74.60 426	99.08 14	87.01 417	99.47 13	56.93 416	98.23 386	78.91 408	95.61 390	94.01 408

MS-PatchMatch		94.83 254	94.91 240	94.57 311	96.81 336	87.10 330	94.23 300	97.34 291	88.74 342	97.14 206	97.11 262	91.94 248	98.23 386	92.99 263	97.92 321	98.37 272

MVS-HIRNet		88.40 374	90.20 355	82.99 404	97.01 329	60.04 429	93.11 342	85.61 419	84.45 391	88.72 410	99.09 55	84.72 329	98.23 386	82.52 397	96.59 372	90.69 419

cascas		91.89 336	91.35 334	93.51 340	94.27 402	85.60 347	88.86 410	98.61 187	79.32 409	92.16 383	91.44 400	89.22 288	98.12 389	90.80 304	97.47 348	96.82 373

MSLP-MVS++		96.42 184	96.71 163	95.57 262	97.82 256	90.56 261	95.71 220	98.84 135	94.72 207	96.71 239	97.39 241	94.91 168	98.10 390	95.28 172	99.02 244	98.05 311

EPMVS		89.26 366	88.55 368	91.39 384	92.36 420	79.11 403	95.65 228	79.86 424	88.60 344	93.12 365	96.53 299	70.73 400	98.10 390	90.75 307	89.32 415	96.98 363

test_fmvs3		97.38 125	97.56 110	96.84 192	98.63 162	92.81 203	97.60 94	99.61 18	90.87 312	98.76 75	99.66 4	94.03 191	97.90 392	99.24 9	99.68 85	99.81 10

mvsany_test3		96.21 190	95.93 206	97.05 174	97.40 310	94.33 152	95.76 219	94.20 359	89.10 335	99.36 28	99.60 8	93.97 193	97.85 393	95.40 170	98.63 285	98.99 194

PMMVS2		93.66 300	94.07 283	92.45 371	97.57 295	80.67 397	86.46 413	96.00 325	93.99 234	97.10 210	97.38 243	89.90 277	97.82 394	88.76 343	99.47 157	98.86 220

1314		92.38 324	92.30 319	92.64 366	95.42 384	85.15 356	95.86 213	96.97 306	85.40 379	90.62 392	93.06 378	91.12 257	97.80 395	86.74 370	95.49 392	94.97 403

TESTMET0.1,1		87.20 385	86.57 385	89.07 396	93.62 412	72.84 425	89.89 402	87.01 416	85.46 378	89.12 408	90.20 409	56.00 420	97.72 396	90.91 300	96.92 357	96.64 378

test_fmvs2		96.38 185	96.45 181	96.16 235	97.85 248	91.30 245	96.81 141	99.45 25	89.24 334	98.49 94	99.38 20	88.68 291	97.62 397	98.83 22	99.32 202	99.57 50

testgi		96.07 195	96.50 180	94.80 300	99.26 57	87.69 319	95.96 206	98.58 192	95.08 194	98.02 153	96.25 314	97.92 21	97.60 398	88.68 346	98.74 273	99.11 175

CMPMVS	73.10 23	92.74 319	91.39 333	96.77 197	93.57 413	94.67 136	94.21 302	97.67 276	80.36 406	93.61 352	96.60 295	82.85 343	97.35 399	84.86 386	98.78 269	98.29 286
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_vis1_n		95.67 214	95.89 208	95.03 286	98.18 217	89.89 267	96.94 134	99.28 35	88.25 350	98.20 129	98.92 73	86.69 313	97.19 400	97.70 62	98.82 266	98.00 316

test_fmvs1_n		95.21 237	95.28 223	94.99 289	98.15 224	89.13 285	96.81 141	99.43 27	86.97 363	97.21 201	98.92 73	83.00 342	97.13 401	98.09 42	98.94 251	98.72 238

mvsany_test1		93.47 305	93.03 302	94.79 301	94.05 408	92.12 224	90.82 393	90.01 407	85.02 384	97.26 198	98.28 148	93.57 202	97.03 402	92.51 270	95.75 389	95.23 401

EMVS		89.06 368	89.22 360	88.61 398	93.00 416	77.34 412	82.91 420	90.92 395	94.64 211	92.63 378	91.81 396	76.30 375	97.02 403	83.83 392	96.90 359	91.48 417

test_fmvs1		94.51 273	94.60 260	94.26 325	95.91 363	87.92 311	95.35 250	99.02 86	86.56 367	96.79 232	98.52 114	82.64 344	97.00 404	97.87 50	98.71 277	97.88 324

PMVS	89.60 17	96.71 169	96.97 148	95.95 244	99.51 28	97.81 20	97.42 110	97.49 287	97.93 56	95.95 283	98.58 107	96.88 82	96.91 405	89.59 332	99.36 187	93.12 413
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		89.52 365	89.78 357	88.73 397	93.14 414	77.61 410	83.26 419	92.02 383	94.82 205	93.71 348	93.11 373	75.31 380	96.81 406	85.81 374	96.81 364	91.77 416

GG-mvs-BLEND				90.60 389	91.00 422	84.21 372	98.23 46	72.63 429		82.76 420	84.11 421	56.14 419	96.79 407	72.20 419	92.09 410	90.78 418

PC_three_1452									87.24 358	98.37 107	97.44 234	97.00 69	96.78 408	92.01 275	99.25 214	99.21 151

MonoMVSNet		93.30 310	93.96 288	91.33 385	94.14 406	81.33 393	97.68 89	96.69 317	95.38 182	96.32 263	98.42 125	84.12 334	96.76 409	90.78 305	92.12 409	95.89 390

new_pmnet		92.34 325	91.69 330	94.32 322	96.23 350	89.16 283	92.27 365	92.88 373	84.39 392	95.29 307	96.35 311	85.66 321	96.74 410	84.53 388	97.56 342	97.05 361

PVSNet_0	81.89 21	84.49 388	83.21 391	88.34 399	95.76 375	74.97 422	83.49 418	92.70 377	78.47 412	87.94 413	86.90 420	83.38 340	96.63 411	73.44 418	66.86 424	93.40 411

ttmdpeth		94.05 289	94.15 281	93.75 334	95.81 371	85.32 351	96.00 200	94.93 351	92.07 288	94.19 332	99.09 55	85.73 320	96.41 412	90.98 297	98.52 292	99.53 61

test_vis3_rt		97.04 141	96.98 147	97.23 161	98.44 189	95.88 84	96.82 140	99.67 10	90.30 321	99.27 33	99.33 28	94.04 190	96.03 413	97.14 81	97.83 326	99.78 14

MVStest1		91.89 336	91.45 331	93.21 348	89.01 425	84.87 361	95.82 217	95.05 349	91.50 302	98.75 76	99.19 39	57.56 414	95.11 414	97.78 56	98.37 303	99.64 39

SD-MVS		97.37 127	97.70 90	96.35 223	98.14 226	95.13 124	96.54 161	98.92 112	95.94 150	99.19 38	98.08 174	97.74 29	95.06 415	95.24 175	99.54 130	98.87 219
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
test_vis1_rt		94.03 291	93.65 292	95.17 280	95.76 375	93.42 188	93.97 316	98.33 220	84.68 387	93.17 364	95.89 331	92.53 233	94.79 416	93.50 251	94.97 395	97.31 357

test_f		95.82 207	95.88 209	95.66 258	97.61 293	93.21 196	95.61 232	98.17 240	86.98 362	98.42 102	99.47 13	90.46 267	94.74 417	97.71 60	98.45 299	99.03 187

test0.0.03 1		90.11 354	89.21 361	92.83 361	93.89 409	86.87 334	91.74 374	88.74 411	92.02 290	94.71 321	91.14 403	73.92 386	94.48 418	83.75 394	92.94 405	97.16 359

dmvs_re		92.08 332	91.27 337	94.51 314	97.16 324	92.79 206	95.65 228	92.64 378	94.11 230	92.74 373	90.98 405	83.41 339	94.44 419	80.72 403	94.07 402	96.29 386

dmvs_testset		87.30 384	86.99 381	88.24 400	96.71 337	77.48 411	94.68 285	86.81 417	92.64 280	89.61 405	87.01 419	85.91 318	93.12 420	61.04 424	88.49 416	94.13 407

wuyk23d		93.25 312	95.20 225	87.40 403	96.07 360	95.38 107	97.04 129	94.97 350	95.33 183	99.70 7	98.11 172	98.14 18	91.94 421	77.76 412	99.68 85	74.89 421

FPMVS		89.92 359	88.63 367	93.82 332	98.37 194	96.94 49	91.58 376	93.34 369	88.00 353	90.32 397	97.10 263	70.87 399	91.13 422	71.91 420	96.16 382	93.39 412

test_method		66.88 390	66.13 393	69.11 406	62.68 431	25.73 434	49.76 422	96.04 324	14.32 426	64.27 426	91.69 398	73.45 391	88.05 423	76.06 414	66.94 423	93.54 409

MVE	73.61 22	86.48 387	85.92 386	88.18 401	96.23 350	85.28 354	81.78 421	75.79 425	86.01 370	82.53 421	91.88 395	92.74 221	87.47 424	71.42 421	94.86 397	91.78 415
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		63.43 391	63.37 394	63.60 407	83.91 429	53.17 431	85.14 415	43.40 433	77.91 415	80.96 423	79.17 423	36.36 431	77.10 425	37.88 426	45.63 425	60.54 422

DeepMVS_CX				77.17 405	90.94 423	85.28 354		74.08 428	52.51 424	80.87 424	88.03 416	75.25 381	70.63 426	59.23 425	84.94 420	75.62 420

kuosan		54.81 393	54.94 396	54.42 408	74.43 430	50.03 432	84.98 416	44.27 432	61.80 423	62.49 427	70.43 424	35.16 432	58.04 427	19.30 427	41.61 426	55.19 423

tmp_tt		57.23 392	62.50 395	41.44 409	34.77 432	49.21 433	83.93 417	60.22 431	15.31 425	71.11 425	79.37 422	70.09 402	44.86 428	64.76 422	82.93 422	30.25 424

testmvs		12.33 396	15.23 399	3.64 411	5.77 434	2.23 436	88.99 409	3.62 434	2.30 429	5.29 429	13.09 426	4.52 434	1.95 429	5.16 429	8.32 428	6.75 426

test123		12.59 395	15.49 398	3.87 410	6.07 433	2.55 435	90.75 394	2.59 435	2.52 428	5.20 430	13.02 427	4.96 433	1.85 430	5.20 428	9.09 427	7.23 425

mmdepth		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

monomultidepth		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

test_blank		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uanet_test		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

DCPMVS		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

cdsmvs_eth3d_5k		24.22 394	32.30 397	0.00 412	0.00 435	0.00 437	0.00 423	98.10 250	0.00 430	0.00 431	95.06 349	97.54 40	0.00 431	0.00 430	0.00 429	0.00 427

pcd_1.5k_mvsjas		7.98 397	10.65 400	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	95.82 132	0.00 431	0.00 430	0.00 429	0.00 427

sosnet-low-res		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

sosnet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uncertanet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

Regformer		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

ab-mvs-re		7.91 398	10.55 401	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	94.94 351	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uanet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

WAC-MVS						79.32 401								85.41 380

FOURS1					99.59 17	98.20 8	99.03 8	99.25 38	98.96 22	98.87 63

test_one_0601					99.05 106	95.50 102		98.87 124	97.21 93	98.03 152	98.30 143	96.93 75

eth-test2					0.00 435

eth-test					0.00 435

RE-MVS-def			97.88 74		98.81 134	98.05 10	97.55 99	98.86 127	97.77 60	98.20 129	98.07 176	96.94 73		95.49 155	99.20 219	99.26 143

IU-MVS					99.22 66	95.40 105		98.14 247	85.77 375	98.36 110				95.23 176	99.51 144	99.49 79

save fliter					98.48 185	94.71 133	94.53 290	98.41 209	95.02 199

test0726					99.24 61	95.51 99	96.89 137	98.89 115	95.92 152	98.64 80	98.31 139	97.06 64

GSMVS																98.06 308

test_part2					99.03 108	96.07 78				98.08 145

sam_mvs1												77.80 364				98.06 308

sam_mvs												77.38 368

MTGPA								98.73 163

MTMP							96.55 160	74.60 426

test9_res														91.29 289	98.89 258	99.00 191

agg_prior2														90.34 322	98.90 255	99.10 179

test_prior4						95.38 107	93.61 329

test_prior2							93.33 337		94.21 224	94.02 340	96.25 314	93.64 201		91.90 278	98.96 248

新几何2							93.43 332

旧先验1					97.80 261	93.87 169		97.75 272			97.04 267	93.57 202			98.68 279	98.72 238

原ACMM2							92.82 346

test222					98.17 220	93.24 195	92.74 350	97.61 285	75.17 418	94.65 322	96.69 291	90.96 261			98.66 282	97.66 340

segment_acmp												95.34 152

testdata1							92.77 347		93.78 238

plane_prior7					98.70 153	94.67 136

plane_prior6					98.38 193	94.37 150						91.91 250

plane_prior4											96.77 286

plane_prior3						94.51 143			95.29 186	96.16 276

plane_prior2							96.50 162		96.36 123

plane_prior1					98.49 183

plane_prior						94.29 153	95.42 241		94.31 223						98.93 253

n2								0.00 436

nn								0.00 436

door-mid								98.17 240

test11								98.08 252

door								97.81 270

HQP5-MVS						92.47 213

HQP-NCC					97.85 248		94.26 295		93.18 261	92.86 370

ACMP_Plane					97.85 248		94.26 295		93.18 261	92.86 370

BP-MVS														90.51 317

HQP3-MVS								98.43 205							98.74 273

HQP2-MVS												90.33 270

NP-MVS					98.14 226	93.72 175					95.08 347

MDTV_nov1_ep13_2view						57.28 430	94.89 276		80.59 404	94.02 340		78.66 361		85.50 379		97.82 328

ACMMP++_ref															99.52 139

ACMMP++															99.55 126

Test By Simon												94.51 180

High-resolution multi-view benchmark