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

UA-Net		98.88 10	98.76 16	99.22 2	99.11 105	97.89 16	99.47 3	99.32 40	99.08 16	97.87 223	99.67 5	96.47 128	99.92 5	97.88 64	99.98 2	99.85 6

reproduce_model		98.54 25	98.33 47	99.15 3	99.06 113	98.04 11	97.04 142	99.09 94	98.42 43	99.03 57	98.71 109	96.93 90	99.83 35	97.09 103	99.63 120	99.56 67

reproduce-ours		98.48 29	98.27 53	99.12 4	98.99 129	98.02 12	96.81 158	99.02 122	98.29 50	98.97 66	98.61 122	97.27 60	99.82 38	96.86 116	99.61 134	99.51 85

our_new_method		98.48 29	98.27 53	99.12 4	98.99 129	98.02 12	96.81 158	99.02 122	98.29 50	98.97 66	98.61 122	97.27 60	99.82 38	96.86 116	99.61 134	99.51 85

MTAPA		98.14 50	97.84 97	99.06 6	99.44 42	97.90 15	97.25 128	98.73 220	97.69 75	97.90 218	97.96 237	95.81 168	99.82 38	96.13 156	99.61 134	99.45 112

mPP-MVS		97.91 84	97.53 143	99.04 7	99.22 78	97.87 17	97.74 93	98.78 209	96.04 178	97.10 280	97.73 272	96.53 123	99.78 58	95.16 234	99.50 197	99.46 108

MSP-MVS		97.45 144	96.92 193	99.03 8	99.26 68	97.70 21	97.66 99	98.89 161	95.65 206	98.51 123	96.46 383	92.15 302	99.81 43	95.14 237	98.58 381	99.58 51
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
SR-MVS-dyc-post		98.14 50	97.84 97	99.02 9	98.81 163	98.05 9	97.55 108	98.86 174	97.77 67	98.20 173	98.07 218	96.60 119	99.76 77	95.49 198	99.20 286	99.26 180

TDRefinement		98.90 8	98.86 11	99.02 9	99.54 28	98.06 8	99.34 5	99.44 33	98.85 27	99.00 62	99.20 40	97.42 52	99.59 201	97.21 96	99.76 72	99.40 134

SR-MVS		98.00 64	97.66 122	99.01 11	98.77 176	97.93 14	97.38 121	98.83 191	97.32 100	98.06 194	97.85 251	96.65 114	99.77 69	95.00 249	99.11 303	99.32 160

MP-MVS		97.64 120	97.18 174	99.00 12	99.32 62	97.77 20	97.49 114	98.73 220	96.27 152	95.59 393	97.75 267	96.30 141	99.78 58	93.70 324	99.48 205	99.45 112
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
Effi-MVS+-dtu		96.81 202	96.09 255	98.99 13	96.90 416	98.69 4	96.42 192	98.09 324	95.86 194	95.15 408	95.54 437	94.26 237	99.81 43	94.06 300	98.51 387	98.47 344

anonymousdsp		98.72 17	98.63 23	98.99 13	99.62 16	97.29 41	98.65 22	99.19 62	95.62 208	99.35 35	99.37 24	97.38 54	99.90 17	98.59 41	99.91 19	99.77 15

CP-MVS		97.92 80	97.56 138	98.99 13	98.99 129	97.82 18	97.93 73	98.96 146	96.11 169	96.89 303	97.45 299	96.85 102	99.78 58	95.19 229	99.63 120	99.38 143

PGM-MVS		97.88 89	97.52 144	98.96 16	99.20 87	97.62 24	97.09 139	99.06 103	95.45 218	97.55 243	97.94 240	97.11 70	99.78 58	94.77 271	99.46 211	99.48 102

RPSCF		97.87 91	97.51 146	98.95 17	99.15 96	98.43 6	97.56 107	99.06 103	96.19 163	98.48 128	98.70 111	94.72 214	99.24 352	94.37 288	99.33 265	99.17 202

XVS		97.96 68	97.63 128	98.94 18	99.15 96	97.66 22	97.77 84	98.83 191	97.42 89	96.32 344	97.64 281	96.49 126	99.72 111	95.66 186	99.37 244	99.45 112

X-MVStestdata		92.86 414	90.83 453	98.94 18	99.15 96	97.66 22	97.77 84	98.83 191	97.42 89	96.32 344	36.50 547	96.49 126	99.72 111	95.66 186	99.37 244	99.45 112

ACMMPR		97.95 72	97.62 130	98.94 18	99.20 87	97.56 28	97.59 105	98.83 191	96.05 176	97.46 254	97.63 282	96.77 107	99.76 77	95.61 192	99.46 211	99.49 96

testf1		98.57 21	98.45 36	98.93 21	99.79 3	98.78 2	97.69 96	99.42 35	97.69 75	98.92 72	98.77 95	97.80 30	99.25 348	96.27 149	99.69 99	98.76 305

APD_test2		98.57 21	98.45 36	98.93 21	99.79 3	98.78 2	97.69 96	99.42 35	97.69 75	98.92 72	98.77 95	97.80 30	99.25 348	96.27 149	99.69 99	98.76 305

ACMMP		98.05 61	97.75 113	98.93 21	99.23 75	97.60 25	98.09 61	98.96 146	95.75 202	97.91 217	98.06 224	96.89 97	99.76 77	95.32 221	99.57 154	99.43 125
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
region2R		97.92 80	97.59 135	98.92 24	99.22 78	97.55 29	97.60 103	98.84 184	96.00 181	97.22 267	97.62 283	96.87 101	99.76 77	95.48 202	99.43 227	99.46 108

HPM-MVS		98.11 55	97.83 100	98.92 24	99.42 45	97.46 35	98.57 23	99.05 109	95.43 223	97.41 257	97.50 295	97.98 23	99.79 53	95.58 195	99.57 154	99.50 88
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HPM-MVS_fast		98.32 38	98.13 59	98.88 26	99.54 28	97.48 34	98.35 39	99.03 118	95.88 192	97.88 220	98.22 196	98.15 20	99.74 95	96.50 132	99.62 123	99.42 127

ACMM	93.33 11	98.05 61	97.79 105	98.85 27	99.15 96	97.55 29	96.68 174	98.83 191	95.21 230	98.36 145	98.13 207	98.13 22	99.62 188	96.04 160	99.54 172	99.39 141
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ZNCC-MVS		97.92 80	97.62 130	98.83 28	99.32 62	97.24 43	97.45 116	98.84 184	95.76 200	96.93 299	97.43 301	97.26 64	99.79 53	96.06 157	99.53 176	99.45 112

HFP-MVS		97.94 76	97.64 126	98.83 28	99.15 96	97.50 33	97.59 105	98.84 184	96.05 176	97.49 248	97.54 289	97.07 75	99.70 136	95.61 192	99.46 211	99.30 166

GST-MVS		97.82 98	97.49 150	98.81 30	99.23 75	97.25 42	97.16 133	98.79 205	95.96 184	97.53 244	97.40 303	96.93 90	99.77 69	95.04 243	99.35 255	99.42 127

HPM-MVS++		96.99 181	96.38 240	98.81 30	98.64 196	97.59 26	95.97 242	98.20 305	95.51 215	95.06 410	96.53 379	94.10 240	99.70 136	94.29 291	99.15 296	99.13 214

APD-MVS_3200maxsize		98.13 54	97.90 89	98.79 32	98.79 169	97.31 40	97.55 108	98.92 155	97.72 72	98.25 168	98.13 207	97.10 71	99.75 85	95.44 207	99.24 284	99.32 160

SteuartSystems-ACMMP		98.02 63	97.76 111	98.79 32	99.43 43	97.21 45	97.15 134	98.90 157	96.58 136	98.08 191	97.87 250	97.02 82	99.76 77	95.25 224	99.59 144	99.40 134
Skip Steuart: Steuart Systems R&D Blog.
APD_test1		97.95 72	97.68 119	98.75 34	99.60 17	98.60 5	97.21 132	99.08 98	96.57 139	98.07 193	98.38 160	96.22 146	99.14 371	94.71 276	99.31 270	98.52 337

mvs_tets		98.90 8	98.94 9	98.75 34	99.69 11	96.48 69	98.54 26	99.22 56	96.23 157	99.71 7	99.48 15	98.77 7	99.93 3	98.89 30	99.95 5	99.84 8

WR-MVS_H		98.65 18	98.62 25	98.75 34	99.51 32	96.61 64	98.55 25	99.17 67	99.05 19	99.17 46	98.79 91	95.47 184	99.89 20	97.95 62	99.91 19	99.75 24

jajsoiax		98.77 12	98.79 15	98.74 37	99.66 13	96.48 69	98.45 34	99.12 81	95.83 197	99.67 10	99.37 24	98.25 17	99.92 5	98.77 33	99.94 8	99.82 9

LPG-MVS_test		97.94 76	97.67 120	98.74 37	99.15 96	97.02 46	97.09 139	99.02 122	95.15 234	98.34 149	98.23 193	97.91 25	99.70 136	94.41 285	99.73 85	99.50 88

LGP-MVS_train				98.74 37	99.15 96	97.02 46		99.02 122	95.15 234	98.34 149	98.23 193	97.91 25	99.70 136	94.41 285	99.73 85	99.50 88

LTVRE_ROB	96.88 1	99.18 2	99.34 2	98.72 40	99.71 10	96.99 48	99.69 2	99.57 21	99.02 21	99.62 15	99.36 26	98.53 11	99.52 226	98.58 42	99.95 5	99.66 38
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
MP-MVS-pluss		97.69 112	97.36 157	98.70 41	99.50 35	96.84 52	95.38 293	98.99 139	92.45 358	98.11 186	98.31 172	97.25 65	99.77 69	96.60 128	99.62 123	99.48 102
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_djsdf		98.73 14	98.74 19	98.69 42	99.63 15	96.30 82	98.67 18	99.02 122	96.50 141	99.32 36	99.44 19	97.43 51	99.92 5	98.73 36	99.95 5	99.86 5

ACMMP_NAP		97.89 88	97.63 128	98.67 43	99.35 58	96.84 52	96.36 200	98.79 205	95.07 238	97.88 220	98.35 164	97.24 66	99.72 111	96.05 159	99.58 150	99.45 112

MIMVSNet1		98.51 28	98.45 36	98.67 43	99.72 8	96.71 57	98.76 16	98.89 161	98.49 40	99.38 31	99.14 52	95.44 186	99.84 33	96.47 133	99.80 63	99.47 106

UniMVSNet_ETH3D		99.12 3	99.28 5	98.65 45	99.77 5	96.34 78	99.18 6	99.20 59	99.67 3	99.73 6	99.65 8	99.15 3	99.86 27	97.22 95	99.92 15	99.77 15

COLMAP_ROB	94.48 6	98.25 44	98.11 62	98.64 46	99.21 85	97.35 39	97.96 68	99.16 69	98.34 46	98.78 89	98.52 136	97.32 57	99.45 261	94.08 299	99.67 108	99.13 214
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		98.61 19	98.61 27	98.63 47	99.77 5	96.35 77	99.17 7	99.05 109	98.05 61	99.61 16	99.52 12	93.72 254	99.88 22	98.72 38	99.88 28	99.65 41

SMA-MVS		97.48 141	97.11 176	98.60 48	98.83 160	96.67 60	96.74 166	98.73 220	91.61 382	98.48 128	98.36 162	96.53 123	99.68 151	95.17 232	99.54 172	99.45 112
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
DTE-MVSNet		98.79 11	98.86 11	98.59 49	99.55 24	96.12 91	98.48 33	99.10 89	99.36 7	99.29 38	99.06 61	97.27 60	99.93 3	97.71 75	99.91 19	99.70 33

LS3D		97.77 104	97.50 148	98.57 50	96.24 437	97.58 27	98.45 34	98.85 180	98.58 36	97.51 246	97.94 240	95.74 171	99.63 183	95.19 229	98.97 318	98.51 338

pmmvs6		99.07 6	99.24 7	98.56 51	99.81 2	96.38 74	98.87 12	99.30 42	99.01 22	99.63 14	99.66 6	99.27 2	99.68 151	97.75 73	99.89 26	99.62 45

lecture		98.59 20	98.60 28	98.55 52	99.48 37	96.38 74	98.08 62	99.09 94	98.46 41	98.68 105	98.73 101	97.88 27	99.80 50	97.43 87	99.59 144	99.48 102

ACMP	92.54 13	97.47 142	97.10 177	98.55 52	99.04 121	96.70 58	96.24 213	98.89 161	93.71 303	97.97 210	97.75 267	97.44 50	99.63 183	93.22 340	99.70 97	99.32 160
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
sc_t1		99.09 5	99.28 5	98.53 54	99.72 8	96.21 86	98.87 12	99.19 62	99.71 2	99.76 4	99.65 8	98.64 9	99.79 53	98.07 56	99.90 25	99.58 51

EGC-MVSNET		83.08 506	77.93 511	98.53 54	99.57 20	97.55 29	98.33 42	98.57 254	4.71 549	10.38 551	98.90 85	95.60 178	99.50 231	95.69 183	99.61 134	98.55 331

DPE-MVS		97.64 120	97.35 158	98.50 56	98.85 157	96.18 87	95.21 311	98.99 139	95.84 196	98.78 89	98.08 216	96.84 103	99.81 43	93.98 307	99.57 154	99.52 81
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
tt0320-xc		99.10 4	99.31 3	98.49 57	99.57 20	96.09 93	98.91 11	99.55 25	99.67 3	99.78 3	99.69 4	98.63 10	99.77 69	98.02 58	99.93 11	99.60 47

XVG-ACMP-BASELINE		97.58 133	97.28 164	98.49 57	99.16 93	96.90 51	96.39 195	98.98 142	95.05 240	98.06 194	98.02 230	95.86 160	99.56 212	94.37 288	99.64 117	99.00 248

CPTT-MVS		96.69 214	96.08 256	98.49 57	98.89 149	96.64 62	97.25 128	98.77 211	92.89 347	96.01 368	97.13 333	92.23 300	99.67 161	92.24 359	99.34 260	99.17 202

APDe-MVS		98.14 50	98.03 73	98.47 60	98.72 183	96.04 96	98.07 63	99.10 89	95.96 184	98.59 114	98.69 112	96.94 88	99.81 43	96.64 122	99.58 150	99.57 59
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
PEN-MVS		98.75 13	98.85 13	98.44 61	99.58 19	95.67 114	98.45 34	99.15 75	99.33 8	99.30 37	99.00 68	97.27 60	99.92 5	97.64 79	99.92 15	99.75 24

tt0320		99.07 6	99.29 4	98.43 62	99.55 24	95.92 103	98.97 10	99.53 27	99.67 3	99.79 2	99.71 3	98.33 14	99.78 58	98.11 52	99.92 15	99.57 59

TranMVSNet+NR-MVSNet		98.33 36	98.30 51	98.43 62	99.07 111	95.87 105	96.73 170	99.05 109	98.67 30	98.84 83	98.45 147	97.58 44	99.88 22	96.45 136	99.86 35	99.54 73

OPM-MVS		97.54 135	97.25 166	98.41 64	99.11 105	96.61 64	95.24 309	98.46 268	94.58 266	98.10 188	98.07 218	97.09 73	99.39 293	95.16 234	99.44 217	99.21 194
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
APD-MVS		97.00 180	96.53 229	98.41 64	98.55 218	96.31 80	96.32 203	98.77 211	92.96 345	97.44 256	97.58 287	95.84 161	99.74 95	91.96 363	99.35 255	99.19 198
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PS-CasMVS		98.73 14	98.85 13	98.39 66	99.55 24	95.47 130	98.49 31	99.13 80	99.22 12	99.22 43	98.96 74	97.35 56	99.92 5	97.79 70	99.93 11	99.79 13

usedtu_dtu_shiyan2		97.54 135	97.26 165	98.37 67	99.54 28	96.04 96	97.94 71	98.06 331	97.36 98	98.62 109	98.20 198	95.52 181	99.73 101	90.90 391	99.18 291	99.33 158

UniMVSNet_NR-MVSNet		97.83 95	97.65 123	98.37 67	98.72 183	95.78 108	95.66 268	99.02 122	98.11 57	98.31 155	97.69 276	94.65 220	99.85 30	97.02 109	99.71 93	99.48 102

DU-MVS		97.79 102	97.60 134	98.36 69	98.73 180	95.78 108	95.65 270	98.87 170	97.57 79	98.31 155	97.83 254	94.69 216	99.85 30	97.02 109	99.71 93	99.46 108

UniMVSNet (Re)		97.83 95	97.65 123	98.35 70	98.80 166	95.86 106	95.92 248	99.04 117	97.51 84	98.22 172	97.81 259	94.68 218	99.78 58	97.14 101	99.75 82	99.41 133

TestfortrainingZip a		98.22 46	98.18 57	98.33 71	99.36 54	95.49 128	97.75 87	98.86 174	97.28 103	98.87 79	98.41 154	96.31 138	99.77 69	97.40 88	99.38 242	99.74 26

CS-MVS		98.09 56	98.01 76	98.32 72	98.45 239	96.69 59	98.52 29	99.69 8	98.07 59	96.07 364	97.19 325	96.88 99	99.86 27	97.50 84	99.73 85	98.41 349

nrg030		98.54 25	98.62 25	98.32 72	99.22 78	95.66 115	97.90 76	99.08 98	98.31 47	99.02 59	98.74 100	97.68 35	99.61 196	97.77 72	99.85 47	99.70 33

DeepPCF-MVS	94.58 5	96.90 191	96.43 235	98.31 74	97.48 381	97.23 44	92.56 441	98.60 246	92.84 348	98.54 119	97.40 303	96.64 116	98.78 420	94.40 287	99.41 235	98.93 270

NormalMVS		96.87 194	96.39 238	98.30 75	99.48 37	95.57 119	96.87 153	98.90 157	96.94 118	96.85 305	97.88 247	85.36 421	99.76 77	95.63 189	99.59 144	99.57 59

CP-MVSNet		98.42 33	98.46 33	98.30 75	99.46 40	95.22 152	98.27 48	98.84 184	99.05 19	99.01 60	98.65 119	95.37 189	99.90 17	97.57 81	99.91 19	99.77 15

XVG-OURS-SEG-HR		97.38 153	97.07 180	98.30 75	99.01 124	97.41 38	94.66 348	99.02 122	95.20 231	98.15 182	97.52 293	98.83 5	98.43 460	94.87 261	96.41 485	99.07 235

MED-MVS		98.14 50	98.09 66	98.27 78	99.36 54	95.35 137	97.75 87	99.30 42	97.28 103	98.88 77	98.41 154	96.99 84	99.73 101	95.36 216	99.51 189	99.74 26

h-mvs33		96.29 241	95.63 286	98.26 79	98.50 230	96.11 92	96.90 151	97.09 388	96.58 136	97.21 269	98.19 199	84.14 432	99.78 58	95.89 172	96.17 493	98.89 278

NR-MVSNet		97.96 68	97.86 96	98.26 79	98.73 180	95.54 122	98.14 58	98.73 220	97.79 66	99.42 28	97.83 254	94.40 231	99.78 58	95.91 171	99.76 72	99.46 108

XVG-OURS		97.12 174	96.74 207	98.26 79	98.99 129	97.45 36	93.82 395	99.05 109	95.19 232	98.32 153	97.70 275	95.22 197	98.41 461	94.27 292	98.13 408	98.93 270

test_0728_SECOND				98.25 82	99.23 75	95.49 128	96.74 166	98.89 161					99.75 85	95.48 202	99.52 183	99.53 78

PHI-MVS		96.96 187	96.53 229	98.25 82	97.48 381	96.50 67	96.76 164	98.85 180	93.52 312	96.19 358	96.85 357	95.94 156	99.42 272	93.79 317	99.43 227	98.83 288

MSC_two_6792asdad				98.22 84	97.75 345	95.34 143		98.16 316					99.75 85	95.87 174	99.51 189	99.57 59

No_MVS				98.22 84	97.75 345	95.34 143		98.16 316					99.75 85	95.87 174	99.51 189	99.57 59

SF-MVS		97.60 125	97.39 153	98.22 84	98.93 141	95.69 112	97.05 141	99.10 89	95.32 227	97.83 226	97.88 247	96.44 131	99.72 111	94.59 282	99.39 240	99.25 187

PS-MVSNAJss		98.53 27	98.63 23	98.21 87	99.68 12	94.82 169	98.10 60	99.21 57	96.91 120	99.75 5	99.45 18	95.82 164	99.92 5	98.80 32	99.96 4	99.89 4

MED-MVS test				98.17 88	99.36 54	95.35 137	97.75 87	99.30 42	94.02 294	98.88 77	97.54 289		99.73 101	95.36 216	99.53 176	99.44 122

SymmetryMVS		96.43 234	95.85 275	98.17 88	98.58 213	95.57 119	96.87 153	95.29 442	96.94 118	96.85 305	97.88 247	85.36 421	99.76 77	95.63 189	99.27 277	99.19 198

ME-MVS		97.53 138	97.32 160	98.16 90	98.70 189	95.35 137	96.04 231	98.60 246	96.16 168	97.99 203	97.54 289	95.94 156	99.70 136	95.36 216	99.53 176	99.44 122

DVP-MVS		97.78 103	97.65 123	98.16 90	99.24 72	95.51 124	96.74 166	98.23 301	95.92 189	98.40 139	98.28 184	97.06 76	99.71 127	95.48 202	99.52 183	99.26 180
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
DeepC-MVS	95.41 4	97.82 98	97.70 115	98.16 90	98.78 173	95.72 110	96.23 214	99.02 122	93.92 299	98.62 109	98.99 70	97.69 34	99.62 188	96.18 154	99.87 33	99.15 206
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	97.73 107	97.59 135	98.15 93	98.11 289	95.60 117	98.04 64	98.70 229	98.13 56	96.93 299	98.45 147	95.30 194	99.62 188	95.64 188	98.96 321	99.24 188

SPE-MVS-test		97.91 84	97.84 97	98.14 94	98.52 222	96.03 100	98.38 38	99.67 9	98.11 57	95.50 399	96.92 354	96.81 105	99.87 25	96.87 115	99.76 72	98.51 338

PM-MVS		97.36 157	97.10 177	98.14 94	98.91 146	96.77 54	96.20 215	98.63 244	93.82 300	98.54 119	98.33 167	93.98 244	99.05 388	95.99 165	99.45 214	98.61 326

DVP-MVS++		97.96 68	97.90 89	98.12 96	97.75 345	95.40 132	99.03 8	98.89 161	96.62 130	98.62 109	98.30 178	96.97 86	99.75 85	95.70 181	99.25 281	99.21 194

NCCC		96.52 224	95.99 262	98.10 97	97.81 329	95.68 113	95.00 329	98.20 305	95.39 224	95.40 403	96.36 390	93.81 250	99.45 261	93.55 329	98.42 396	99.17 202

DKM-HiRes		96.47 229	95.93 269	98.09 98	98.86 155	96.41 73	94.38 358	98.56 255	94.05 292	96.93 299	97.48 296	87.73 387	98.55 449	95.86 176	99.48 205	99.31 165

SED-MVS		97.94 76	97.90 89	98.07 99	99.22 78	95.35 137	96.79 162	98.83 191	96.11 169	99.08 54	98.24 191	97.87 28	99.72 111	95.44 207	99.51 189	99.14 212

Vis-MVSNet		98.27 42	98.34 45	98.07 99	99.33 60	95.21 154	98.04 64	99.46 31	97.32 100	97.82 227	99.11 54	96.75 108	99.86 27	97.84 67	99.36 249	99.15 206
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_fmvsmconf0.01_n		98.57 21	98.74 19	98.06 101	99.39 50	94.63 177	96.70 173	99.82 1	95.44 221	99.64 13	99.52 12	98.96 4	99.74 95	99.38 7	99.86 35	99.81 10

AllTest		97.20 167	96.92 193	98.06 101	99.08 109	96.16 88	97.14 136	99.16 69	94.35 279	97.78 229	98.07 218	95.84 161	99.12 376	91.41 377	99.42 230	98.91 274

TestCases				98.06 101	99.08 109	96.16 88		99.16 69	94.35 279	97.78 229	98.07 218	95.84 161	99.12 376	91.41 377	99.42 230	98.91 274

N_pmnet		95.18 314	94.23 356	98.06 101	97.85 313	96.55 66	92.49 442	91.63 500	89.34 436	98.09 189	97.41 302	90.33 335	99.06 387	91.58 376	99.31 270	98.56 329

F-COLMAP		95.30 308	94.38 351	98.05 105	98.64 196	96.04 96	95.61 276	98.66 238	89.00 445	93.22 471	96.40 388	92.90 279	99.35 312	87.45 462	97.53 449	98.77 303

test_fmvsmconf0.1_n		98.41 34	98.54 30	98.03 106	99.16 93	94.61 178	96.18 216	99.73 5	95.05 240	99.60 17	99.34 29	98.68 8	99.72 111	99.21 12	99.85 47	99.76 21

CNVR-MVS		96.92 189	96.55 226	98.03 106	98.00 300	95.54 122	94.87 335	98.17 312	94.60 263	96.38 341	97.05 340	95.67 175	99.36 308	95.12 240	99.08 308	99.19 198

TSAR-MVS + MP.		97.42 150	97.23 168	98.00 108	99.38 52	95.00 162	97.63 102	98.20 305	93.00 340	98.16 180	98.06 224	95.89 159	99.72 111	95.67 185	99.10 306	99.28 174
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test_fmvsmconf_n		98.30 40	98.41 39	97.99 109	98.94 137	94.60 179	96.00 236	99.64 16	94.99 245	99.43 27	99.18 45	98.51 12	99.71 127	99.13 20	99.84 50	99.67 36

RoMa-HiRes		97.28 161	97.05 183	97.98 110	98.78 173	96.22 85	96.48 189	98.47 266	93.69 305	98.97 66	97.73 272	93.48 260	98.47 457	96.31 145	99.51 189	99.26 180

ACMH+	93.58 10	98.23 45	98.31 49	97.98 110	99.39 50	95.22 152	97.55 108	99.20 59	98.21 54	99.25 41	98.51 139	98.21 18	99.40 284	94.79 268	99.72 90	99.32 160

v7n		98.73 14	98.99 8	97.95 112	99.64 14	94.20 200	98.67 18	99.14 78	99.08 16	99.42 28	99.23 38	96.53 123	99.91 13	99.27 10	99.93 11	99.73 28

Anonymous20231211		98.55 24	98.76 16	97.94 113	98.79 169	94.37 191	98.84 14	99.15 75	99.37 6	99.67 10	99.43 20	95.61 177	99.72 111	98.12 51	99.86 35	99.73 28

OMC-MVS		96.48 228	96.00 261	97.91 114	98.30 256	96.01 101	94.86 336	98.60 246	91.88 374	97.18 273	97.21 324	96.11 151	99.04 390	90.49 411	99.34 260	98.69 315

GeoE		97.75 105	97.70 115	97.89 115	98.88 150	94.53 183	97.10 138	98.98 142	95.75 202	97.62 238	97.59 285	97.61 43	99.77 69	96.34 143	99.44 217	99.36 153

train_agg		95.46 297	94.66 331	97.88 116	97.84 320	95.23 149	93.62 407	98.39 282	87.04 471	93.78 449	95.99 417	94.58 223	99.52 226	91.76 373	98.90 332	98.89 278

pm-mvs1		98.47 31	98.67 21	97.86 117	99.52 31	94.58 180	98.28 46	99.00 134	97.57 79	99.27 39	99.22 39	98.32 15	99.50 231	97.09 103	99.75 82	99.50 88

ITE_SJBPF				97.85 118	98.64 196	96.66 61		98.51 260	95.63 207	97.22 267	97.30 318	95.52 181	98.55 449	90.97 388	98.90 332	98.34 362

CDPH-MVS		95.45 298	94.65 332	97.84 119	98.28 260	94.96 164	93.73 401	98.33 291	85.03 495	95.44 400	96.60 375	95.31 193	99.44 264	90.01 418	99.13 299	99.11 225

DP-MVS		97.87 91	97.89 92	97.81 120	98.62 207	94.82 169	97.13 137	98.79 205	98.98 23	98.74 97	98.49 140	95.80 169	99.49 237	95.04 243	99.44 217	99.11 225

RoMa-SfM		96.87 194	96.56 222	97.79 121	98.50 230	96.46 71	95.89 250	98.45 269	91.48 393	98.84 83	97.40 303	93.93 247	97.96 481	94.99 255	99.58 150	98.96 260

fmvsm_l_conf0.5_n_3		98.29 41	98.46 33	97.79 121	98.90 148	94.05 205	96.06 228	99.63 17	96.07 174	99.37 32	98.93 78	98.29 16	99.68 151	99.11 22	99.79 65	99.65 41

hse-mvs2		95.77 273	95.09 303	97.79 121	97.84 320	95.51 124	95.66 268	95.43 438	96.58 136	97.21 269	96.16 404	84.14 432	99.54 220	95.89 172	96.92 465	98.32 363

EC-MVSNet		97.90 86	97.94 88	97.79 121	98.66 195	95.14 158	98.31 43	99.66 12	97.57 79	95.95 370	97.01 346	96.99 84	99.82 38	97.66 78	99.64 117	98.39 352

MAR-MVS		94.21 364	93.03 394	97.76 125	96.94 414	97.44 37	96.97 147	97.15 382	87.89 464	92.00 495	92.73 494	92.14 303	99.12 376	83.92 503	97.51 450	96.73 468
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
AUN-MVS		93.95 376	92.69 408	97.74 126	97.80 333	95.38 134	95.57 279	95.46 437	91.26 401	92.64 488	96.10 411	74.67 494	99.55 217	93.72 323	96.97 464	98.30 368

VDD-MVS		97.37 155	97.25 166	97.74 126	98.69 192	94.50 186	97.04 142	95.61 432	98.59 35	98.51 123	98.72 102	92.54 293	99.58 204	96.02 162	99.49 200	99.12 220

mmtdpeth		98.33 36	98.53 31	97.71 128	99.07 111	93.44 230	98.80 15	99.78 4	99.10 15	96.61 325	99.63 10	95.42 187	99.73 101	98.53 43	99.86 35	99.95 2

Anonymous20240529		97.96 68	98.04 72	97.71 128	98.69 192	94.28 198	97.86 78	98.31 295	98.79 28	99.23 42	98.86 89	95.76 170	99.61 196	95.49 198	99.36 249	99.23 190

VPA-MVSNet		98.27 42	98.46 33	97.70 130	99.06 113	93.80 214	97.76 86	99.00 134	98.40 44	99.07 56	98.98 71	96.89 97	99.75 85	97.19 99	99.79 65	99.55 71

IS-MVSNet		96.93 188	96.68 210	97.70 130	99.25 71	94.00 207	98.57 23	96.74 406	98.36 45	98.14 184	97.98 236	88.23 378	99.71 127	93.10 344	99.72 90	99.38 143

CSCG		97.40 151	97.30 161	97.69 132	98.95 134	94.83 168	97.28 127	98.99 139	96.35 151	98.13 185	95.95 421	95.99 155	99.66 169	94.36 290	99.73 85	98.59 327

HQP_MVS		96.66 216	96.33 243	97.68 133	98.70 189	94.29 195	96.50 183	98.75 217	96.36 149	96.16 360	96.77 364	91.91 312	99.46 253	92.59 352	99.20 286	99.28 174

Elysia		98.19 47	98.37 40	97.66 134	99.28 64	93.52 226	97.35 123	98.90 157	98.63 32	99.45 24	98.32 170	94.31 234	99.91 13	99.19 14	99.88 28	99.54 73

StellarMVS		98.19 47	98.37 40	97.66 134	99.28 64	93.52 226	97.35 123	98.90 157	98.63 32	99.45 24	98.32 170	94.31 234	99.91 13	99.19 14	99.88 28	99.54 73

EPP-MVSNet		96.84 197	96.58 219	97.65 136	99.18 91	93.78 216	98.68 17	96.34 414	97.91 64	97.30 261	98.06 224	88.46 371	99.85 30	93.85 313	99.40 236	99.32 160

OPU-MVS				97.64 137	98.01 296	95.27 147	96.79 162				97.35 313	96.97 86	98.51 453	91.21 383	99.25 281	99.14 212

MM		96.87 194	96.62 213	97.62 138	97.72 350	93.30 235	96.39 195	92.61 488	97.90 65	96.76 313	98.64 120	90.46 332	99.81 43	99.16 18	99.94 8	99.76 21

MVS_111021_LR		96.82 201	96.55 226	97.62 138	98.27 263	95.34 143	93.81 397	98.33 291	94.59 265	96.56 330	96.63 374	96.61 117	98.73 426	94.80 267	99.34 260	98.78 294

DKM		96.39 237	95.99 262	97.59 140	98.44 240	96.42 72	94.42 357	98.51 260	92.81 349	98.15 182	97.47 297	89.37 359	97.26 493	95.02 248	99.68 104	99.09 231

UGNet		96.81 202	96.56 222	97.58 141	96.64 422	93.84 213	97.75 87	97.12 384	96.47 145	93.62 458	98.88 87	93.22 267	99.53 223	95.61 192	99.69 99	99.36 153
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
FC-MVSNet-test		98.16 49	98.37 40	97.56 142	99.49 36	93.10 242	98.35 39	99.21 57	98.43 42	98.89 75	98.83 90	94.30 236	99.81 43	97.87 65	99.91 19	99.77 15

MCST-MVS		96.24 246	95.80 278	97.56 142	98.75 178	94.13 202	94.66 348	98.17 312	90.17 428	96.21 356	96.10 411	95.14 202	99.43 268	94.13 298	98.85 340	99.13 214

GBi-Net		96.99 181	96.80 203	97.56 142	97.96 302	93.67 219	98.23 50	98.66 238	95.59 210	97.99 203	99.19 41	89.51 353	99.73 101	94.60 279	99.44 217	99.30 166

test1		96.99 181	96.80 203	97.56 142	97.96 302	93.67 219	98.23 50	98.66 238	95.59 210	97.99 203	99.19 41	89.51 353	99.73 101	94.60 279	99.44 217	99.30 166

FMVSNet1		97.95 72	98.08 67	97.56 142	99.14 103	93.67 219	98.23 50	98.66 238	97.41 93	99.00 62	99.19 41	95.47 184	99.73 101	95.83 178	99.76 72	99.30 166

DenseAffine		96.06 256	95.57 288	97.53 147	98.44 240	95.79 107	94.20 372	98.14 319	92.44 360	97.95 213	97.18 327	88.87 366	97.96 481	93.41 331	99.52 183	98.85 287

PMatch-Up-SfM		95.95 263	95.43 291	97.51 148	97.90 310	95.17 156	93.40 418	98.78 209	92.45 358	98.24 169	98.07 218	87.10 399	99.18 363	94.87 261	98.10 409	98.19 381

PMatch-SfM		95.65 286	95.03 307	97.51 148	97.96 302	95.00 162	93.49 414	98.51 260	92.24 364	97.80 228	98.03 228	83.97 437	99.19 360	94.77 271	98.50 388	98.35 361

sd_testset		97.97 66	98.12 60	97.51 148	99.41 46	93.44 230	97.96 68	98.25 298	98.58 36	98.78 89	99.39 21	98.21 18	99.56 212	92.65 350	99.86 35	99.52 81

TransMVSNet (Re)		98.38 35	98.67 21	97.51 148	99.51 32	93.39 234	98.20 55	98.87 170	98.23 53	99.48 21	99.27 34	98.47 13	99.55 217	96.52 131	99.53 176	99.60 47

PLC	91.02 16	94.05 371	92.90 399	97.51 148	98.00 300	95.12 160	94.25 365	98.25 298	86.17 480	91.48 501	95.25 448	91.01 322	99.19 360	85.02 494	96.69 478	98.22 378
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMH	93.61 9	98.44 32	98.76 16	97.51 148	99.43 43	93.54 225	98.23 50	99.05 109	97.40 94	99.37 32	99.08 60	98.79 6	99.47 246	97.74 74	99.71 93	99.50 88
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
alignmvs		96.01 260	95.52 290	97.50 154	97.77 342	94.71 171	96.07 226	96.84 400	97.48 86	96.78 312	94.28 469	85.50 420	99.40 284	96.22 151	98.73 365	98.40 350

Baseline_NR-MVSNet		97.72 110	97.79 105	97.50 154	99.56 22	93.29 236	95.44 285	98.86 174	98.20 55	98.37 142	99.24 36	94.69 216	99.55 217	95.98 166	99.79 65	99.65 41

3Dnovator	96.53 2	97.61 124	97.64 126	97.50 154	97.74 348	93.65 223	98.49 31	98.88 168	96.86 122	97.11 279	98.55 133	95.82 164	99.73 101	95.94 168	99.42 230	99.13 214

ArgMatch-SfM		95.74 277	95.15 300	97.49 157	97.82 327	95.16 157	94.03 383	98.41 278	89.33 437	97.58 240	96.65 372	90.07 342	98.89 407	93.17 342	99.30 273	98.44 348

TSAR-MVS + GP.		96.47 229	96.12 253	97.49 157	97.74 348	95.23 149	94.15 375	96.90 399	93.26 323	98.04 197	96.70 369	94.41 229	98.89 407	94.77 271	99.14 297	98.37 355

ArgMatch-Sym		95.60 290	94.97 310	97.48 159	97.70 353	95.41 131	93.60 411	97.89 340	89.33 437	97.70 233	96.03 416	91.00 324	98.66 438	92.25 358	99.18 291	98.39 352

FIs		97.93 79	98.07 68	97.48 159	99.38 52	92.95 246	98.03 66	99.11 84	98.04 62	98.62 109	98.66 115	93.75 253	99.78 58	97.23 94	99.84 50	99.73 28

test_0402		97.84 94	97.97 80	97.47 161	99.19 89	94.07 203	96.71 171	98.73 220	98.66 31	98.56 117	98.41 154	96.84 103	99.69 144	94.82 265	99.81 59	98.64 319

test_prior				97.46 162	97.79 338	94.26 199		98.42 277					99.34 315			98.79 293

test12				97.46 162	97.61 367	94.07 203		97.78 350		93.57 462		93.31 265	99.42 272		98.78 352	98.89 278

DeepC-MVS_fast	94.34 7	96.74 207	96.51 231	97.44 164	97.69 354	94.15 201	96.02 234	98.43 274	93.17 333	97.30 261	97.38 310	95.48 183	99.28 340	93.74 319	99.34 260	98.88 282
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_fmvsm_n_1920		98.08 57	98.29 52	97.43 165	98.88 150	93.95 209	96.17 220	99.57 21	95.66 205	99.52 20	98.71 109	97.04 80	99.64 178	99.21 12	99.87 33	98.69 315

Anonymous202405211		96.34 240	95.98 264	97.43 165	98.25 266	93.85 212	96.74 166	94.41 457	97.72 72	98.37 142	98.03 228	87.15 397	99.53 223	94.06 300	99.07 310	98.92 273

pmmvs-eth3d		96.49 227	96.18 252	97.42 167	98.25 266	94.29 195	94.77 343	98.07 330	89.81 432	97.97 210	98.33 167	93.11 271	99.08 385	95.46 205	99.84 50	98.89 278

VDDNet		96.98 184	96.84 199	97.41 168	99.40 49	93.26 238	97.94 71	95.31 441	99.26 11	98.39 141	99.18 45	87.85 385	99.62 188	95.13 239	99.09 307	99.35 157

EG-PatchMatch MVS		97.69 112	97.79 105	97.40 169	99.06 113	93.52 226	95.96 244	98.97 145	94.55 267	98.82 86	98.76 99	97.31 58	99.29 335	97.20 98	99.44 217	99.38 143

TestfortrainingZip				97.39 170	97.24 401	94.58 180	97.75 87	97.64 363	96.08 173	96.48 335	96.31 394	92.56 289	99.27 343		96.62 480	98.31 365

Fast-Effi-MVS+-dtu		96.44 232	96.12 253	97.39 170	97.18 403	94.39 188	95.46 283	98.73 220	96.03 180	94.72 422	94.92 456	96.28 144	99.69 144	93.81 316	97.98 416	98.09 388

LF4IMVS		96.07 254	95.63 286	97.36 172	98.19 273	95.55 121	95.44 285	98.82 199	92.29 363	95.70 389	96.55 377	92.63 287	98.69 433	91.75 374	99.33 265	97.85 413

Gipuma		98.07 59	98.31 49	97.36 172	99.76 7	96.28 83	98.51 30	99.10 89	98.76 29	96.79 308	99.34 29	96.61 117	98.82 416	96.38 140	99.50 197	96.98 454
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MGCNet		95.71 279	95.18 298	97.33 174	94.85 503	92.82 248	95.36 294	90.89 510	95.51 215	95.61 392	97.82 257	88.39 373	99.78 58	98.23 50	99.91 19	99.40 134

LCM-MVSNet-Re		97.33 158	97.33 159	97.32 175	98.13 288	93.79 215	96.99 146	99.65 13	96.74 127	99.47 23	98.93 78	96.91 94	99.84 33	90.11 416	99.06 313	98.32 363

LuminaMVS		96.76 206	96.58 219	97.30 176	98.94 137	92.96 245	96.17 220	96.15 416	95.54 214	98.96 69	98.18 202	87.73 387	99.80 50	97.98 60	99.61 134	99.15 206

sasdasda		97.23 165	97.21 170	97.30 176	97.65 362	94.39 188	97.84 79	99.05 109	97.42 89	96.68 317	93.85 475	97.63 41	99.33 317	96.29 147	98.47 391	98.18 383

canonicalmvs		97.23 165	97.21 170	97.30 176	97.65 362	94.39 188	97.84 79	99.05 109	97.42 89	96.68 317	93.85 475	97.63 41	99.33 317	96.29 147	98.47 391	98.18 383

fmvsm_l_conf0.5_n		97.68 115	97.81 103	97.27 179	98.92 143	92.71 257	95.89 250	99.41 38	93.36 318	99.00 62	98.44 149	96.46 130	99.65 172	99.09 23	99.76 72	99.45 112

MVS_111021_HR		96.73 209	96.54 228	97.27 179	98.35 252	93.66 222	93.42 416	98.36 287	94.74 254	96.58 327	96.76 366	96.54 122	98.99 396	94.87 261	99.27 277	99.15 206

SixPastTwentyTwo		97.49 140	97.57 137	97.26 181	99.56 22	92.33 265	98.28 46	96.97 396	98.30 49	99.45 24	99.35 28	88.43 372	99.89 20	98.01 59	99.76 72	99.54 73

KD-MVS_self_test		97.86 93	98.07 68	97.25 182	99.22 78	92.81 250	97.55 108	98.94 151	97.10 109	98.85 81	98.88 87	95.03 206	99.67 161	97.39 90	99.65 113	99.26 180

新几何1				97.25 182	98.29 257	94.70 173		97.73 352	77.98 534	94.83 418	96.67 371	92.08 306	99.45 261	88.17 450	98.65 375	97.61 432

KinetiMVS		97.82 98	98.02 74	97.24 184	99.24 72	92.32 267	96.92 149	98.38 284	98.56 39	99.03 57	98.33 167	93.22 267	99.83 35	98.74 35	99.71 93	99.57 59

test_vis3_rt		97.04 178	96.98 186	97.23 185	98.44 240	95.88 104	96.82 157	99.67 9	90.30 422	99.27 39	99.33 31	94.04 241	96.03 510	97.14 101	97.83 428	99.78 14

Casviewmamba		97.95 72	98.20 56	97.18 186	98.85 157	92.74 255	96.71 171	99.23 51	98.07 59	98.55 118	98.47 145	97.38 54	99.44 264	96.95 112	99.62 123	99.38 143

fmvsm_s_conf0.1_n_a		97.80 101	98.01 76	97.18 186	99.17 92	92.51 260	96.57 177	99.15 75	93.68 307	98.89 75	99.30 32	96.42 133	99.37 304	99.03 25	99.83 55	99.66 38

WR-MVS		96.90 191	96.81 201	97.16 188	98.56 217	92.20 275	94.33 360	98.12 322	97.34 99	98.20 173	97.33 315	92.81 280	99.75 85	94.79 268	99.81 59	99.54 73

TAMVS		95.49 293	94.94 312	97.16 188	98.31 255	93.41 233	95.07 322	96.82 402	91.09 404	97.51 246	97.82 257	89.96 343	99.42 272	88.42 445	99.44 217	98.64 319

CDS-MVSNet		94.88 330	94.12 363	97.14 190	97.64 365	93.57 224	93.96 390	97.06 390	90.05 429	96.30 350	96.55 377	86.10 412	99.47 246	90.10 417	99.31 270	98.40 350
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
fmvsm_s_conf0.5_n_a		97.65 119	97.83 100	97.13 191	98.80 166	92.51 260	96.25 211	99.06 103	93.67 308	98.64 107	99.00 68	96.23 145	99.36 308	98.99 27	99.80 63	99.53 78

fmvsm_l_conf0.5_n_a		97.60 125	97.76 111	97.11 192	98.92 143	92.28 269	95.83 255	99.32 40	93.22 325	98.91 74	98.49 140	96.31 138	99.64 178	99.07 24	99.76 72	99.40 134

SDMVSNet		97.97 66	98.26 55	97.11 192	99.41 46	92.21 272	96.92 149	98.60 246	98.58 36	98.78 89	99.39 21	97.80 30	99.62 188	94.98 257	99.86 35	99.52 81

tt0805		97.44 146	97.56 138	97.11 192	99.55 24	96.36 76	98.66 21	95.66 428	98.31 47	97.09 285	95.45 442	97.17 69	98.50 454	98.67 39	97.45 454	96.48 476

EI-MVSNet-Vis-set		97.32 159	97.39 153	97.11 192	97.36 391	92.08 281	95.34 298	97.65 359	97.74 70	98.29 158	98.11 212	95.05 204	99.68 151	97.50 84	99.50 197	99.56 67

EI-MVSNet-UG-set		97.32 159	97.40 152	97.09 196	97.34 394	92.01 285	95.33 299	97.65 359	97.74 70	98.30 157	98.14 205	95.04 205	99.69 144	97.55 82	99.52 183	99.58 51

MGCFI-Net		97.20 167	97.23 168	97.08 197	97.68 355	93.71 218	97.79 82	99.09 94	97.40 94	96.59 326	93.96 472	97.67 36	99.35 312	96.43 138	98.50 388	98.17 385

XXY-MVS		97.54 135	97.70 115	97.07 198	99.46 40	92.21 272	97.22 131	99.00 134	94.93 249	98.58 115	98.92 81	97.31 58	99.41 282	94.44 283	99.43 227	99.59 50

mvsany_test3		96.21 248	95.93 269	97.05 199	97.40 389	94.33 193	95.76 260	94.20 461	89.10 442	99.36 34	99.60 11	93.97 245	97.85 485	95.40 214	98.63 376	98.99 252

lessismore_v0				97.05 199	99.36 54	92.12 277		84.07 541		98.77 94	98.98 71	85.36 421	99.74 95	97.34 93	99.37 244	99.30 166

fmvsm_s_conf0.5_n_5		97.63 122	97.83 100	97.04 201	98.77 176	92.33 265	95.63 275	99.58 19	93.53 311	99.10 52	98.66 115	96.44 131	99.65 172	99.12 21	99.68 104	99.12 220

TAPA-MVS	93.32 12	94.93 326	94.23 356	97.04 201	98.18 276	94.51 184	95.22 310	98.73 220	81.22 520	96.25 353	95.95 421	93.80 251	98.98 398	89.89 421	98.87 337	97.62 431
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
casdiffseed414692147		97.67 117	97.88 94	97.03 203	98.82 162	92.32 267	96.55 180	99.17 67	96.99 111	98.01 201	98.67 114	97.64 39	99.38 297	95.45 206	99.66 111	99.40 134

EPNet		93.72 383	92.62 411	97.03 203	87.61 549	92.25 270	96.27 207	91.28 505	96.74 127	87.65 532	97.39 308	85.00 425	99.64 178	92.14 361	99.48 205	99.20 197
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchMatch-RL		94.61 346	93.81 371	97.02 205	98.19 273	95.72 110	93.66 404	97.23 377	88.17 459	94.94 415	95.62 435	91.43 315	98.57 446	87.36 463	97.68 440	96.76 467

casdiffmvs_mvg		97.83 95	98.11 62	97.00 206	98.57 215	92.10 280	95.97 242	99.18 64	97.67 78	99.00 62	98.48 144	97.64 39	99.50 231	96.96 111	99.54 172	99.40 134
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ALIKED-LG		94.42 355	93.57 378	96.97 207	96.80 418	97.51 32	96.56 179	98.87 170	90.23 426	96.16 360	96.93 351	83.76 438	97.07 496	84.00 502	98.80 349	96.33 480

fmvsm_s_conf0.5_n_9		97.98 65	98.32 48	96.96 208	98.92 143	91.45 300	95.87 252	99.53 27	97.44 87	99.56 18	99.05 62	95.34 190	99.67 161	99.52 2	99.70 97	99.77 15

K. test v3		96.44 232	96.28 246	96.95 209	99.41 46	91.53 295	97.65 100	90.31 520	98.89 26	98.93 71	99.36 26	84.57 430	99.92 5	97.81 68	99.56 159	99.39 141

tfpnnormal		97.72 110	97.97 80	96.94 210	99.26 68	92.23 271	97.83 81	98.45 269	98.25 52	99.13 50	98.66 115	96.65 114	99.69 144	93.92 310	99.62 123	98.91 274

test_fmvsmvis_n_1920		98.08 57	98.47 32	96.93 211	99.03 122	93.29 236	96.32 203	99.65 13	95.59 210	99.71 7	99.01 67	97.66 38	99.60 199	99.44 5	99.83 55	97.90 409

MVP-Stereo		95.69 280	95.28 294	96.92 212	98.15 283	93.03 243	95.64 274	98.20 305	90.39 419	96.63 324	97.73 272	91.63 314	99.10 383	91.84 368	97.31 459	98.63 321
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HQP-MVS		95.17 316	94.58 340	96.92 212	97.85 313	92.47 262	94.26 362	98.43 274	93.18 330	92.86 481	95.08 450	90.33 335	99.23 354	90.51 409	98.74 362	99.05 240

HyFIR lowres test		93.72 383	92.65 409	96.91 214	98.93 141	91.81 291	91.23 482	98.52 258	82.69 509	96.46 338	96.52 381	80.38 462	99.90 17	90.36 413	98.79 350	99.03 244

GDP-MVS		95.39 301	94.89 317	96.90 215	98.26 265	91.91 287	96.48 189	99.28 46	95.06 239	96.54 333	97.12 335	74.83 493	99.82 38	97.19 99	99.27 277	98.96 260

BP-MVS1		95.36 303	94.86 320	96.89 216	98.35 252	91.72 292	96.76 164	95.21 443	96.48 144	96.23 354	97.19 325	75.97 489	99.80 50	97.91 63	99.60 141	99.15 206

VNet		96.84 197	96.83 200	96.88 217	98.06 291	92.02 284	96.35 201	97.57 368	97.70 74	97.88 220	97.80 260	92.40 298	99.54 220	94.73 274	98.96 321	99.08 232

FMVSNet2		96.72 211	96.67 211	96.87 218	97.96 302	91.88 288	97.15 134	98.06 331	95.59 210	98.50 125	98.62 121	89.51 353	99.65 172	94.99 255	99.60 141	99.07 235

FE-MVSNET2		97.69 112	97.97 80	96.85 219	99.19 89	91.46 299	97.04 142	99.11 84	95.85 195	98.73 99	99.02 66	96.66 111	99.68 151	96.31 145	99.86 35	99.40 134

fmvsm_s_conf0.1_n		97.73 107	98.02 74	96.85 219	99.09 108	91.43 302	96.37 199	99.11 84	94.19 285	99.01 60	99.25 35	96.30 141	99.38 297	99.00 26	99.88 28	99.73 28

EIA-MVS		96.04 257	95.77 280	96.85 219	97.80 333	92.98 244	96.12 223	99.16 69	94.65 261	93.77 451	91.69 507	95.68 173	99.67 161	94.18 295	98.85 340	97.91 408

test_fmvs3		97.38 153	97.56 138	96.84 222	98.63 205	92.81 250	97.60 103	99.61 18	90.87 409	98.76 95	99.66 6	94.03 242	97.90 484	99.24 11	99.68 104	99.81 10

viewdifsd2359ckpt09		96.23 247	96.04 258	96.82 223	98.29 257	92.06 283	95.25 308	99.03 118	91.51 390	96.19 358	97.01 346	94.41 229	99.40 284	93.76 318	98.90 332	99.00 248

ETV-MVS		96.13 253	95.90 271	96.82 223	97.76 343	93.89 210	95.40 290	98.95 148	95.87 193	95.58 394	91.00 513	96.36 137	99.72 111	93.36 333	98.83 344	96.85 461

fmvsm_s_conf0.5_n		97.62 123	97.89 92	96.80 225	98.79 169	91.44 301	96.14 222	99.06 103	94.19 285	98.82 86	98.98 71	96.22 146	99.38 297	98.98 28	99.86 35	99.58 51

DP-MVS Recon		95.55 291	95.13 301	96.80 225	98.51 224	93.99 208	94.60 350	98.69 230	90.20 427	95.78 385	96.21 401	92.73 283	98.98 398	90.58 407	98.86 339	97.42 441

QAPM		95.88 267	95.57 288	96.80 225	97.90 310	91.84 290	98.18 57	98.73 220	88.41 454	96.42 339	98.13 207	94.73 213	99.75 85	88.72 439	98.94 324	98.81 290

CMPMVS	73.10 23	92.74 417	91.39 439	96.77 228	93.57 525	94.67 174	94.21 371	97.67 355	80.36 524	93.61 459	96.60 375	82.85 446	97.35 492	84.86 496	98.78 352	98.29 371
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Fast-Effi-MVS+		95.49 293	95.07 304	96.75 229	97.67 359	92.82 248	94.22 370	98.60 246	91.61 382	93.42 468	92.90 488	96.73 109	99.70 136	92.60 351	97.89 425	97.74 422

CNLPA		95.04 322	94.47 346	96.75 229	97.81 329	95.25 148	94.12 379	97.89 340	94.41 277	94.57 425	95.69 431	90.30 338	98.35 467	86.72 469	98.76 360	96.64 469

Effi-MVS+		96.19 250	96.01 260	96.71 231	97.43 387	92.19 276	96.12 223	99.10 89	95.45 218	93.33 470	94.71 460	97.23 67	99.56 212	93.21 341	97.54 448	98.37 355

pmmvs4		94.82 332	94.19 360	96.70 232	97.42 388	92.75 254	92.09 459	96.76 404	86.80 476	95.73 388	97.22 323	89.28 360	98.89 407	93.28 337	99.14 297	98.46 346

CLD-MVS		95.47 296	95.07 304	96.69 233	98.27 263	92.53 259	91.36 474	98.67 235	91.22 402	95.78 385	94.12 470	95.65 176	98.98 398	90.81 394	99.72 90	98.57 328
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
V42		97.04 178	97.16 175	96.68 234	98.59 211	91.05 309	96.33 202	98.36 287	94.60 263	97.99 203	98.30 178	93.32 264	99.62 188	97.40 88	99.53 176	99.38 143

SSM_0404		97.47 142	97.75 113	96.64 235	98.81 163	91.26 305	96.57 177	99.16 69	96.95 116	98.44 134	98.09 214	97.05 78	99.72 111	95.21 227	99.44 217	98.95 263

hybridcas		97.73 107	98.10 65	96.62 236	98.84 159	91.10 308	96.46 191	99.20 59	97.53 83	98.65 106	98.42 151	97.41 53	99.38 297	96.79 118	99.59 144	99.37 152

fmvsm_s_conf0.5_n_10		97.74 106	98.11 62	96.62 236	98.72 183	90.95 316	95.99 239	99.50 29	96.22 158	99.20 44	98.93 78	95.13 203	99.77 69	99.49 3	99.76 72	99.15 206

LFMVS		95.32 307	94.88 319	96.62 236	98.03 292	91.47 298	97.65 100	90.72 514	99.11 14	97.89 219	98.31 172	79.20 469	99.48 240	93.91 311	99.12 302	98.93 270

viewdifsd2359ckpt13		96.47 229	96.42 236	96.61 239	98.35 252	91.50 297	95.31 302	98.84 184	93.21 327	96.73 314	97.58 287	95.28 195	99.26 345	94.02 305	98.45 393	99.07 235

fmvsm_s_conf0.5_n_11		97.90 86	98.34 45	96.60 240	98.75 178	90.50 330	96.28 205	99.56 23	97.05 110	99.15 48	99.11 54	96.31 138	99.69 144	98.97 29	99.84 50	99.62 45

ab-mvs		96.59 219	96.59 218	96.60 240	98.64 196	92.21 272	98.35 39	97.67 355	94.45 275	96.99 293	98.79 91	94.96 211	99.49 237	90.39 412	99.07 310	98.08 389

VPNet		97.26 163	97.49 150	96.59 242	99.47 39	90.58 323	96.27 207	98.53 257	97.77 67	98.46 131	98.41 154	94.59 222	99.68 151	94.61 278	99.29 274	99.52 81

原ACMM1				96.58 243	98.16 281	92.12 277		98.15 318	85.90 484	93.49 464	96.43 385	92.47 297	99.38 297	87.66 455	98.62 377	98.23 376

AdaColmap		95.11 318	94.62 336	96.58 243	97.33 396	94.45 187	94.92 332	98.08 326	93.15 335	93.98 447	95.53 439	94.34 233	99.10 383	85.69 482	98.61 378	96.20 484

fmvsm_l_conf0.5_n_9		97.92 80	98.37 40	96.57 245	98.94 137	90.54 326	95.39 291	99.58 19	96.82 123	99.56 18	98.77 95	97.23 67	99.61 196	99.17 17	99.86 35	99.57 59

PCF-MVS	89.43 18	92.12 436	90.64 457	96.57 245	97.80 333	93.48 229	89.88 511	98.45 269	74.46 540	96.04 367	95.68 432	90.71 329	99.31 327	73.73 536	99.01 317	96.91 458
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ambc				96.56 247	98.23 269	91.68 294	97.88 77	98.13 321		98.42 136	98.56 132	94.22 238	99.04 390	94.05 302	99.35 255	98.95 263

casdiffmvs		97.50 139	97.81 103	96.56 247	98.51 224	91.04 310	95.83 255	99.09 94	97.23 105	98.33 152	98.30 178	97.03 81	99.37 304	96.58 130	99.38 242	99.28 174
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
mamba_0408		97.17 169	97.38 155	96.55 249	98.51 224	90.96 313	95.19 312	99.06 103	96.60 132	98.27 160	97.78 262	96.58 120	99.72 111	95.04 243	99.40 236	98.98 255

SSM_0407		97.39 152	97.67 120	96.54 250	98.51 224	90.96 313	96.40 193	99.16 69	96.95 116	98.27 160	98.09 214	97.05 78	99.67 161	95.21 227	99.40 236	98.98 255

LoFTR		95.39 301	95.01 308	96.52 251	97.16 404	95.19 155	94.77 343	96.95 398	90.31 421	98.78 89	98.29 182	86.71 404	97.91 483	92.56 354	99.57 154	96.46 478

mvs5depth		98.06 60	98.58 29	96.51 252	98.97 133	89.65 356	99.43 4	99.81 2	99.30 9	98.36 145	99.86 2	93.15 269	99.88 22	98.50 44	99.84 50	99.99 1

FMVSNet5		93.39 395	92.35 416	96.50 253	95.83 464	90.81 320	97.31 125	98.27 296	92.74 351	96.27 351	98.28 184	62.23 523	99.67 161	90.86 392	99.36 249	99.03 244

CANet		95.86 269	95.65 285	96.49 254	96.41 432	90.82 318	94.36 359	98.41 278	94.94 247	92.62 490	96.73 367	92.68 284	99.71 127	95.12 240	99.60 141	98.94 266

test20.03		96.58 222	96.61 215	96.48 255	98.49 232	91.72 292	95.68 266	97.69 354	96.81 124	98.27 160	97.92 243	94.18 239	98.71 430	90.78 396	99.66 111	99.00 248

E4		97.28 161	97.55 141	96.46 256	98.86 155	90.53 328	95.28 307	99.18 64	95.82 198	98.01 201	98.59 127	96.78 106	99.46 253	95.86 176	99.56 159	99.38 143

E5new		97.59 128	97.96 86	96.45 257	99.01 124	90.45 332	96.50 183	99.23 51	96.19 163	98.27 160	98.72 102	97.49 46	99.47 246	96.64 122	99.62 123	99.42 127

E6new		97.59 128	97.97 80	96.45 257	99.01 124	90.45 332	96.50 183	99.23 51	96.20 159	98.27 160	98.72 102	97.49 46	99.47 246	96.64 122	99.62 123	99.42 127

E6		97.59 128	97.97 80	96.45 257	99.01 124	90.45 332	96.50 183	99.23 51	96.20 159	98.27 160	98.72 102	97.49 46	99.47 246	96.64 122	99.62 123	99.42 127

E5		97.59 128	97.96 86	96.45 257	99.01 124	90.45 332	96.50 183	99.23 51	96.19 163	98.27 160	98.72 102	97.49 46	99.47 246	96.64 122	99.62 123	99.42 127

fmvsm_s_conf0.5_n_6		97.45 144	97.79 105	96.44 261	98.58 213	90.31 338	95.77 259	99.33 39	94.52 268	98.85 81	98.44 149	95.68 173	99.62 188	99.15 19	99.81 59	99.38 143

UnsupCasMVSNet_eth		95.91 266	95.73 281	96.44 261	98.48 234	91.52 296	95.31 302	98.45 269	95.76 200	97.48 251	97.54 289	89.53 352	98.69 433	94.43 284	94.61 517	99.13 214

viewmacassd2359aftdt		97.25 164	97.52 144	96.43 263	98.83 160	90.49 331	95.45 284	99.18 64	95.44 221	97.98 208	98.47 145	96.90 96	99.37 304	95.93 169	99.55 166	99.43 125

baseline		97.44 146	97.78 109	96.43 263	98.52 222	90.75 321	96.84 155	99.03 118	96.51 140	97.86 224	98.02 230	96.67 110	99.36 308	97.09 103	99.47 208	99.19 198

SSM_04072		97.14 170	97.38 155	96.42 265	98.51 224	90.96 313	95.19 312	99.06 103	96.60 132	98.27 160	97.78 262	96.58 120	99.31 327	95.04 243	99.40 236	98.98 255

DPM-MVS		93.68 386	92.77 406	96.42 265	97.91 309	92.54 258	91.17 485	97.47 371	84.99 497	93.08 474	94.74 459	89.90 344	99.00 394	87.54 458	98.09 411	97.72 425

PVSNet_Blended_VisFu		95.95 263	95.80 278	96.42 265	99.28 64	90.62 322	95.31 302	99.08 98	88.40 455	96.97 297	98.17 204	92.11 304	99.78 58	93.64 325	99.21 285	98.86 285

FE-MVSNET		96.59 219	96.65 212	96.41 268	98.94 137	90.51 329	96.07 226	99.05 109	92.94 346	98.03 198	98.00 234	93.08 272	99.42 272	94.04 303	99.74 84	99.30 166

fmvsm_s_conf0.5_n_3		97.88 89	98.37 40	96.41 268	98.73 180	89.82 350	95.94 246	99.49 30	96.81 124	99.09 53	99.03 65	97.09 73	99.65 172	99.37 8	99.76 72	99.76 21

ANet_high		98.31 39	98.94 9	96.41 268	99.33 60	89.64 357	97.92 74	99.56 23	99.27 10	99.66 12	99.50 14	97.67 36	99.83 35	97.55 82	99.98 2	99.77 15

mvsmamba		94.91 327	94.41 350	96.40 271	97.65 362	91.30 303	97.92 74	95.32 440	91.50 391	95.54 396	98.38 160	83.06 444	99.68 151	92.46 356	97.84 427	98.23 376

fmvsm_s_conf0.5_n_4		97.43 148	97.77 110	96.39 272	98.48 234	89.89 348	95.65 270	99.26 48	94.73 257	98.72 100	98.58 128	95.58 179	99.57 210	99.28 9	99.67 108	99.73 28

SD-MVS		97.37 155	97.70 115	96.35 273	98.14 285	95.13 159	96.54 182	98.92 155	95.94 187	99.19 45	98.08 216	97.74 33	95.06 518	95.24 225	99.54 172	98.87 284
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
E2		96.97 185	97.19 172	96.33 274	98.64 196	90.34 336	95.07 322	99.12 81	95.00 243	97.66 236	98.31 172	96.19 148	99.43 268	95.35 219	99.35 255	99.23 190

E3		96.97 185	97.19 172	96.33 274	98.64 196	90.34 336	95.07 322	99.12 81	95.00 243	97.66 236	98.31 172	96.19 148	99.43 268	95.35 219	99.35 255	99.23 190

Patchmtry		95.03 324	94.59 339	96.33 274	94.83 505	90.82 318	96.38 198	97.20 379	96.59 135	97.49 248	98.57 130	77.67 476	99.38 297	92.95 347	99.62 123	98.80 291

OpenMVS	94.22 8	95.48 295	95.20 296	96.32 277	97.16 404	91.96 286	97.74 93	98.84 184	87.26 467	94.36 431	98.01 232	93.95 246	99.67 161	90.70 403	98.75 361	97.35 444

v10		97.55 134	97.97 80	96.31 278	98.60 209	89.64 357	97.44 117	99.02 122	96.60 132	98.72 100	99.16 49	93.48 260	99.72 111	98.76 34	99.92 15	99.58 51

PMMVS		92.39 426	91.08 446	96.30 279	93.12 529	92.81 250	90.58 498	95.96 422	79.17 529	91.85 497	92.27 499	90.29 339	98.66 438	89.85 422	96.68 479	97.43 440

viewmanbaseed2359cas		96.77 205	96.94 190	96.27 280	98.41 247	90.24 339	95.11 317	99.03 118	94.28 282	97.45 255	97.85 251	95.92 158	99.32 325	95.18 231	99.19 290	99.24 188

fmvsm_s_conf0.5_n_8		97.66 118	98.12 60	96.27 280	98.79 169	89.43 363	95.76 260	99.42 35	97.49 85	99.16 47	99.04 63	94.56 225	99.69 144	99.18 16	99.73 85	99.70 33

viewcassd2359sk11		96.73 209	96.89 197	96.24 282	98.46 238	90.20 340	94.94 331	99.07 102	94.43 276	97.33 260	98.05 227	95.69 172	99.40 284	94.98 257	99.11 303	99.12 220

v8		97.60 125	98.06 71	96.23 283	98.71 187	89.44 362	97.43 119	98.82 199	97.29 102	98.74 97	99.10 56	93.86 248	99.68 151	98.61 40	99.94 8	99.56 67

1112_ss		94.12 367	93.42 384	96.23 283	98.59 211	90.85 317	94.24 367	98.85 180	85.49 488	92.97 476	94.94 454	86.01 413	99.64 178	91.78 372	97.92 420	98.20 380

FMVSNet3		95.26 310	94.94 312	96.22 285	96.53 426	90.06 342	95.99 239	97.66 357	94.11 289	97.99 203	97.91 245	80.22 467	99.63 183	94.60 279	99.44 217	98.96 260

AstraMVS		96.41 236	96.48 233	96.20 286	98.91 146	89.69 354	96.28 205	93.29 476	96.11 169	98.70 102	98.36 162	89.41 357	99.66 169	97.60 80	99.63 120	99.26 180

fmvsm_s_conf0.1_n_2		97.68 115	98.18 57	96.20 286	99.06 113	89.08 375	95.51 281	99.72 6	96.06 175	99.48 21	99.24 36	95.18 199	99.60 199	99.45 4	99.88 28	99.94 3

114514_t		93.96 374	93.22 388	96.19 288	99.06 113	90.97 312	95.99 239	98.94 151	73.88 541	93.43 467	96.93 351	92.38 299	99.37 304	89.09 433	99.28 275	98.25 375

CHOSEN 1792x2688		94.10 368	93.41 385	96.18 289	99.16 93	90.04 344	92.15 455	98.68 232	79.90 525	96.22 355	97.83 254	87.92 384	99.42 272	89.18 432	99.65 113	99.08 232

E3new		96.50 225	96.61 215	96.17 290	98.28 260	90.09 341	94.85 337	99.02 122	93.95 298	97.01 291	97.74 270	95.19 198	99.39 293	94.70 277	98.77 359	99.04 242

fmvsm_s_conf0.5_n_2		97.59 128	98.07 68	96.17 290	98.78 173	89.10 374	95.33 299	99.55 25	95.96 184	99.41 30	99.10 56	95.18 199	99.59 201	99.43 6	99.86 35	99.81 10

test_fmvs2		96.38 238	96.45 234	96.16 292	97.85 313	91.30 303	96.81 158	99.45 32	89.24 441	98.49 126	99.38 23	88.68 369	97.62 489	98.83 31	99.32 267	99.57 59

v1192		96.83 200	97.06 181	96.15 293	98.28 260	89.29 365	95.36 294	98.77 211	93.73 302	98.11 186	98.34 166	93.02 278	99.67 161	98.35 48	99.58 150	99.50 88

gbinet_0.2-2-1-0.02		92.86 414	91.78 432	96.13 294	94.34 511	90.06 342	91.90 463	96.63 412	91.73 376	94.24 433	86.22 539	80.26 466	99.56 212	93.87 312	96.80 473	98.77 303

v1144		96.84 197	97.08 179	96.13 294	98.42 245	89.28 366	95.41 289	98.67 235	94.21 283	97.97 210	98.31 172	93.06 273	99.65 172	98.06 57	99.62 123	99.45 112

UnsupCasMVSNet_bld		94.72 338	94.26 355	96.08 296	98.62 207	90.54 326	93.38 419	98.05 333	90.30 422	97.02 289	96.80 363	89.54 349	99.16 369	88.44 444	96.18 492	98.56 329

onestephybrid01		96.25 245	96.31 244	96.07 297	97.54 375	90.01 346	94.06 382	98.77 211	94.74 254	96.32 344	97.74 270	94.03 242	99.20 358	94.81 266	98.79 350	98.98 255

fmvsm_s_conf0.5_n_7		97.13 171	97.50 148	96.04 298	98.43 243	89.03 378	94.92 332	99.00 134	94.51 269	98.42 136	98.96 74	94.97 210	99.54 220	98.42 46	99.85 47	99.56 67

v144192		96.69 214	96.90 196	96.03 299	98.25 266	88.92 379	95.49 282	98.77 211	93.05 338	98.09 189	98.29 182	92.51 296	99.70 136	98.11 52	99.56 159	99.47 106

ALIKED-MNN		93.09 410	92.12 423	96.00 300	96.50 427	96.72 56	95.52 280	98.20 305	82.37 513	90.90 504	96.15 405	87.02 400	96.30 508	83.03 510	99.42 230	94.99 502

v1921920		96.72 211	96.96 189	95.99 301	98.21 270	88.79 385	95.42 287	98.79 205	93.22 325	98.19 177	98.26 189	92.68 284	99.70 136	98.34 49	99.55 166	99.49 96

DELS-MVS		96.17 251	96.23 248	95.99 301	97.55 374	90.04 344	92.38 450	98.52 258	94.13 287	96.55 332	97.06 339	94.99 208	99.58 204	95.62 191	99.28 275	98.37 355
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
guyue		96.21 248	96.29 245	95.98 303	98.80 166	89.14 372	96.40 193	94.34 459	95.99 183	98.58 115	98.13 207	87.42 393	99.64 178	97.39 90	99.55 166	99.16 205

CANet_DTU		94.65 343	94.21 359	95.96 304	95.90 459	89.68 355	93.92 392	97.83 348	93.19 329	90.12 515	95.64 434	88.52 370	99.57 210	93.27 338	99.47 208	98.62 322

PAPM_NR		94.61 346	94.17 361	95.96 304	98.36 251	91.23 306	95.93 247	97.95 334	92.98 341	93.42 468	94.43 467	90.53 330	98.38 464	87.60 456	96.29 490	98.27 372

v2v482		96.78 204	97.06 181	95.95 306	98.57 215	88.77 386	95.36 294	98.26 297	95.18 233	97.85 225	98.23 193	92.58 288	99.63 183	97.80 69	99.69 99	99.45 112

PMVS	89.60 17	96.71 213	96.97 187	95.95 306	99.51 32	97.81 19	97.42 120	97.49 369	97.93 63	95.95 370	98.58 128	96.88 99	96.91 500	89.59 426	99.36 249	93.12 518
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MSDG		95.33 306	95.13 301	95.94 308	97.40 389	91.85 289	91.02 490	98.37 286	95.30 228	96.31 349	95.99 417	94.51 227	98.38 464	89.59 426	97.65 445	97.60 433

ELoFTR		95.12 317	94.86 320	95.91 309	98.39 248	93.23 240	94.57 352	97.21 378	87.26 467	98.53 122	98.52 136	86.67 407	97.37 491	93.24 339	99.36 249	97.12 449

v1240		96.74 207	97.02 185	95.91 309	98.18 276	88.52 390	95.39 291	98.88 168	93.15 335	98.46 131	98.40 159	92.80 281	99.71 127	98.45 45	99.49 200	99.49 96

SP-LightGlue		95.19 313	94.96 311	95.89 311	95.10 494	94.93 166	94.29 361	98.47 266	94.91 251	94.92 417	95.51 440	86.69 405	95.61 512	97.08 106	97.67 441	97.12 449

Anonymous20231206		95.27 309	95.06 306	95.88 312	98.72 183	89.37 364	95.70 263	97.85 343	88.00 462	96.98 296	97.62 283	91.95 309	99.34 315	89.21 431	99.53 176	98.94 266

Vis-MVSNet (Re-imp)		95.11 318	94.85 322	95.87 313	99.12 104	89.17 367	97.54 113	94.92 449	96.50 141	96.58 327	97.27 319	83.64 439	99.48 240	88.42 445	99.67 108	98.97 259

CL-MVSNet_self_test		95.04 322	94.79 328	95.82 314	97.51 378	89.79 351	91.14 486	96.82 402	93.05 338	96.72 315	96.40 388	90.82 326	99.16 369	91.95 364	98.66 373	98.50 341

IterMVS-LS		96.92 189	97.29 162	95.79 315	98.51 224	88.13 408	95.10 318	98.66 238	96.99 111	98.46 131	98.68 113	92.55 291	99.74 95	96.91 113	99.79 65	99.50 88
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVSMamba_PlusPlus		97.43 148	97.98 79	95.78 316	98.88 150	89.70 353	98.03 66	98.85 180	99.18 13	96.84 307	99.12 53	93.04 274	99.91 13	98.38 47	99.55 166	97.73 423

viewdifsd2359ckpt07		97.10 176	97.55 141	95.76 317	98.64 196	88.58 389	94.54 353	99.11 84	96.96 115	98.54 119	98.18 202	96.91 94	99.44 264	95.58 195	99.49 200	99.26 180

Anonymous20240521		97.07 177	97.51 146	95.76 317	99.35 58	88.18 405	97.78 83	98.40 281	97.11 108	98.34 149	99.04 63	89.58 348	99.79 53	98.09 54	99.93 11	99.30 166

viewmamba		96.62 218	96.92 193	95.74 319	97.85 313	88.83 383	94.25 365	99.00 134	95.69 204	97.18 273	97.90 246	95.34 190	99.29 335	96.20 152	98.85 340	99.11 225

EI-MVSNet		96.63 217	96.93 191	95.74 319	97.26 399	88.13 408	95.29 305	97.65 359	96.99 111	97.94 215	98.19 199	92.55 291	99.58 204	96.91 113	99.56 159	99.50 88

MDA-MVSNet-bldmvs		95.69 280	95.67 283	95.74 319	98.48 234	88.76 387	92.84 431	97.25 376	96.00 181	97.59 239	97.95 239	91.38 316	99.46 253	93.16 343	96.35 488	98.99 252

sss		94.22 362	93.72 374	95.74 319	97.71 352	89.95 347	93.84 394	96.98 395	88.38 456	93.75 452	95.74 430	87.94 380	98.89 407	91.02 386	98.10 409	98.37 355

blended_shiyan8		93.34 398	92.55 413	95.73 323	95.69 474	89.08 375	92.36 451	97.11 385	91.47 394	95.42 402	88.94 527	82.26 450	99.48 240	93.84 314	95.81 501	98.62 322

blended_shiyan6		93.34 398	92.54 414	95.73 323	95.68 475	89.08 375	92.35 452	97.10 386	91.47 394	95.37 404	88.96 526	82.26 450	99.48 240	93.83 315	95.85 497	98.62 322

usedtu_blend_shiyan5		93.74 380	93.08 392	95.71 325	94.99 497	89.17 367	97.38 121	98.93 153	96.40 146	94.75 419	87.24 533	80.36 463	99.40 284	91.84 368	95.85 497	98.55 331

testdata				95.70 326	98.16 281	90.58 323		97.72 353	80.38 523	95.62 390	97.02 342	92.06 307	98.98 398	89.06 435	98.52 384	97.54 436

viewdifsd2359ckpt11		97.13 171	97.62 130	95.67 327	98.64 196	88.36 396	94.84 338	98.95 148	96.24 155	98.70 102	98.61 122	96.66 111	99.29 335	96.46 134	99.45 214	99.36 153

viewmsd2359difaftdt		97.13 171	97.62 130	95.67 327	98.64 196	88.36 396	94.84 338	98.95 148	96.24 155	98.70 102	98.61 122	96.66 111	99.29 335	96.46 134	99.45 214	99.36 153

test_f		95.82 271	95.88 273	95.66 329	97.61 367	93.21 241	95.61 276	98.17 312	86.98 473	98.42 136	99.47 16	90.46 332	94.74 522	97.71 75	98.45 393	99.03 244

BridgeMVS		96.88 193	97.29 162	95.63 330	97.66 360	89.47 361	97.95 70	98.89 161	95.94 187	97.77 231	98.55 133	92.23 300	99.68 151	97.05 108	99.61 134	97.73 423

blend_shiyan4		88.73 484	86.43 499	95.61 331	95.31 488	89.17 367	92.13 456	97.10 386	91.59 388	94.15 439	87.38 532	52.97 546	99.40 284	91.84 368	75.42 545	98.27 372

test_yl		94.40 356	94.00 366	95.59 332	96.95 412	89.52 359	94.75 345	95.55 435	96.18 166	96.79 308	96.14 408	81.09 458	99.18 363	90.75 398	97.77 430	98.07 391

DCV-MVSNet		94.40 356	94.00 366	95.59 332	96.95 412	89.52 359	94.75 345	95.55 435	96.18 166	96.79 308	96.14 408	81.09 458	99.18 363	90.75 398	97.77 430	98.07 391

usedtu_dtu_shiyan1		94.61 346	94.29 353	95.57 334	97.93 307	88.45 391	91.30 479	97.64 363	91.61 382	95.85 381	95.79 428	86.65 408	99.48 240	92.92 348	98.97 318	98.78 294

FE-MVSNET3		94.61 346	94.29 353	95.57 334	97.93 307	88.45 391	91.30 479	97.64 363	91.61 382	95.85 381	95.79 428	86.65 408	99.48 240	92.92 348	98.97 318	98.78 294

diffmvs_AUTHOR		96.50 225	96.81 201	95.57 334	98.03 292	88.26 400	93.73 401	99.14 78	94.92 250	97.24 266	97.84 253	94.62 221	99.33 317	96.44 137	99.37 244	99.13 214

tttt0517		93.31 400	92.56 412	95.57 334	98.71 187	87.86 415	97.44 117	87.17 536	95.79 199	97.47 253	96.84 358	64.12 521	99.81 43	96.20 152	99.32 267	99.02 247

MSLP-MVS++		96.42 235	96.71 208	95.57 334	97.82 327	90.56 325	95.71 262	98.84 184	94.72 258	96.71 316	97.39 308	94.91 212	98.10 478	95.28 222	99.02 315	98.05 398

thisisatest0530		92.71 418	91.76 433	95.56 339	98.42 245	88.23 401	96.03 233	87.35 535	94.04 293	96.56 330	95.47 441	64.03 522	99.77 69	94.78 270	99.11 303	98.68 318

patch_mono-2		96.59 219	96.93 191	95.55 340	98.88 150	87.12 434	94.47 355	99.30 42	94.12 288	96.65 323	98.41 154	94.98 209	99.87 25	95.81 180	99.78 69	99.66 38

Test_1112_low_res		93.53 392	92.86 400	95.54 341	98.60 209	88.86 382	92.75 434	98.69 230	82.66 511	92.65 487	96.92 354	84.75 427	99.56 212	90.94 389	97.76 433	98.19 381

SP-SuperGlue		95.41 300	95.38 292	95.51 342	94.92 502	94.67 174	94.09 380	97.93 337	95.45 218	95.62 390	96.26 397	89.54 349	95.26 514	96.70 120	97.92 420	96.61 472

pmmvs5		94.63 345	94.34 352	95.50 343	97.63 366	88.34 398	94.02 384	97.13 383	87.15 470	95.22 407	97.15 328	87.50 389	99.27 343	93.99 306	99.26 280	98.88 282

MVSFormer		96.14 252	96.36 241	95.49 344	97.68 355	87.81 419	98.67 18	99.02 122	96.50 141	94.48 429	96.15 405	86.90 401	99.92 5	98.73 36	99.13 299	98.74 307

ET-MVSNet_ETH3D		91.12 452	89.67 466	95.47 345	96.41 432	89.15 371	91.54 471	90.23 521	89.07 443	86.78 536	92.84 491	69.39 515	99.44 264	94.16 296	96.61 481	97.82 415

diffmvs		96.04 257	96.23 248	95.46 346	97.35 392	88.03 411	93.42 416	99.08 98	94.09 291	96.66 321	96.93 351	93.85 249	99.29 335	96.01 164	98.67 371	99.06 238
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
v148		96.58 222	96.97 187	95.42 347	98.63 205	87.57 423	95.09 319	97.90 339	95.91 191	98.24 169	97.96 237	93.42 262	99.39 293	96.04 160	99.52 183	99.29 173

OpenMVS_ROB	91.80 14	93.64 389	93.05 393	95.42 347	97.31 398	91.21 307	95.08 321	96.68 409	81.56 517	96.88 304	96.41 386	90.44 334	99.25 348	85.39 487	97.67 441	95.80 492

jason		94.39 358	94.04 365	95.41 349	98.29 257	87.85 417	92.74 436	96.75 405	85.38 492	95.29 405	96.15 405	88.21 379	99.65 172	94.24 293	99.34 260	98.74 307
jason: jason.
wanda-best-256-512		92.66 419	91.75 434	95.40 350	94.99 497	88.19 402	90.89 491	97.05 391	91.02 407	94.75 419	87.24 533	80.36 463	99.46 253	93.63 326	95.85 497	98.55 331

FE-blended-shiyan7		92.66 419	91.75 434	95.40 350	94.99 497	88.19 402	90.89 491	97.05 391	91.02 407	94.75 419	87.24 533	80.36 463	99.46 253	93.63 326	95.85 497	98.55 331

hybridnocas07		96.00 261	96.21 250	95.39 352	97.56 372	87.89 414	93.70 403	98.93 153	93.96 297	96.48 335	97.65 279	93.38 263	99.19 360	95.39 215	98.81 348	99.08 232

balanced_ft_v1		96.29 241	96.60 217	95.38 353	96.77 419	88.73 388	98.44 37	98.44 273	94.97 246	95.91 372	98.77 95	91.03 321	99.75 85	96.16 155	98.91 331	97.65 428

dtuplus		95.73 278	95.86 274	95.33 354	97.72 350	87.82 418	93.74 399	98.60 246	92.12 366	97.27 263	97.92 243	94.35 232	99.13 375	92.24 359	98.83 344	99.05 240

API-MVS		95.09 321	95.01 308	95.31 355	96.61 423	94.02 206	96.83 156	97.18 381	95.60 209	95.79 383	94.33 468	94.54 226	98.37 466	85.70 481	98.52 384	93.52 514

PVSNet_BlendedMVS		95.02 325	94.93 314	95.27 356	97.79 338	87.40 429	94.14 377	98.68 232	88.94 446	94.51 427	98.01 232	93.04 274	99.30 331	89.77 423	99.49 200	99.11 225

lupinMVS		93.77 378	93.28 386	95.24 357	97.68 355	87.81 419	92.12 457	96.05 418	84.52 501	94.48 429	95.06 452	86.90 401	99.63 183	93.62 328	99.13 299	98.27 372

hybrid		95.77 273	95.95 268	95.23 358	97.54 375	87.44 426	93.65 405	98.86 174	93.17 333	96.06 366	97.65 279	93.14 270	99.20 358	94.94 259	98.57 382	99.04 242

D2MVS		95.18 314	95.17 299	95.21 359	97.76 343	87.76 421	94.15 375	97.94 335	89.77 433	96.99 293	97.68 277	87.45 390	99.14 371	95.03 247	99.81 59	98.74 307

Patchmatch-RL test		94.66 342	94.49 344	95.19 360	98.54 220	88.91 380	92.57 440	98.74 219	91.46 396	98.32 153	97.75 267	77.31 481	98.81 418	96.06 157	99.61 134	97.85 413

WTY-MVS		93.55 391	93.00 396	95.19 360	97.81 329	87.86 415	93.89 393	96.00 420	89.02 444	94.07 442	95.44 443	86.27 411	99.33 317	87.69 454	96.82 471	98.39 352

viewmambaseed2359dif		95.68 282	95.85 275	95.17 362	97.51 378	87.41 428	93.61 409	98.58 252	91.06 405	96.68 317	97.66 278	94.71 215	99.11 379	93.93 309	98.94 324	98.99 252

test_vis1_rt		94.03 373	93.65 376	95.17 362	95.76 471	93.42 232	93.97 389	98.33 291	84.68 499	93.17 472	95.89 424	92.53 295	94.79 520	93.50 330	94.97 513	97.31 446

FE-MVS		92.95 413	92.22 419	95.11 364	97.21 402	88.33 399	98.54 26	93.66 470	89.91 431	96.21 356	98.14 205	70.33 513	99.50 231	87.79 452	98.24 404	97.51 437

JIA-IIPM		91.79 444	90.69 456	95.11 364	93.80 522	90.98 311	94.16 374	91.78 499	96.38 147	90.30 512	99.30 32	72.02 507	98.90 406	88.28 447	90.17 532	95.45 498

MIMVSNet		93.42 394	92.86 400	95.10 366	98.17 279	88.19 402	98.13 59	93.69 467	92.07 368	95.04 413	98.21 197	80.95 460	99.03 393	81.42 516	98.06 412	98.07 391

PAPR		92.22 433	91.27 443	95.07 367	95.73 473	88.81 384	91.97 461	97.87 342	85.80 485	90.91 503	92.73 494	91.16 318	98.33 468	79.48 522	95.76 506	98.08 389

MVSTER		94.21 364	93.93 370	95.05 368	95.83 464	86.46 443	95.18 314	97.65 359	92.41 361	97.94 215	98.00 234	72.39 506	99.58 204	96.36 141	99.56 159	99.12 220

test_vis1_n		95.67 283	95.89 272	95.03 369	98.18 276	89.89 348	96.94 148	99.28 46	88.25 458	98.20 173	98.92 81	86.69 405	97.19 494	97.70 77	98.82 346	98.00 403

ALIKED-NN		90.94 458	89.58 467	95.02 370	94.61 508	96.31 80	93.16 427	97.27 375	79.38 527	86.25 537	95.27 447	83.42 441	94.29 528	79.08 524	97.77 430	94.46 506

cl____		94.73 334	94.64 333	95.01 371	95.85 463	87.00 436	91.33 476	98.08 326	93.34 320	97.10 280	97.33 315	84.01 436	99.30 331	95.14 237	99.56 159	98.71 314

DIV-MVS_self_test		94.73 334	94.64 333	95.01 371	95.86 462	87.00 436	91.33 476	98.08 326	93.34 320	97.10 280	97.34 314	84.02 435	99.31 327	95.15 236	99.55 166	98.72 310

test_fmvs1_n		95.21 311	95.28 294	94.99 373	98.15 283	89.13 373	96.81 158	99.43 34	86.97 474	97.21 269	98.92 81	83.00 445	97.13 495	98.09 54	98.94 324	98.72 310

FA-MVS(test-final)		94.91 327	94.89 317	94.99 373	97.51 378	88.11 410	98.27 48	95.20 444	92.40 362	96.68 317	98.60 126	83.44 440	99.28 340	93.34 334	98.53 383	97.59 434

SP-DiffGlue		94.64 344	94.54 343	94.97 375	93.53 526	94.33 193	93.94 391	97.84 345	93.35 319	96.58 327	95.54 437	88.87 366	94.71 523	93.73 321	97.44 455	95.87 489

TinyColmap		96.00 261	96.34 242	94.96 376	97.90 310	87.91 413	94.13 378	98.49 263	94.41 277	98.16 180	97.76 264	96.29 143	98.68 436	90.52 408	99.42 230	98.30 368

PVSNet_Blended		93.96 374	93.65 376	94.91 377	97.79 338	87.40 429	91.43 473	98.68 232	84.50 502	94.51 427	94.48 466	93.04 274	99.30 331	89.77 423	98.61 378	98.02 401

BH-RMVSNet		94.56 350	94.44 349	94.91 377	97.57 370	87.44 426	93.78 398	96.26 415	93.69 305	96.41 340	96.50 382	92.10 305	99.00 394	85.96 479	97.71 437	98.31 365

RPMNet		94.68 341	94.60 337	94.90 379	95.44 482	88.15 406	96.18 216	98.86 174	97.43 88	94.10 440	98.49 140	79.40 468	99.76 77	95.69 183	95.81 501	96.81 465

HY-MVS	91.43 15	92.58 422	91.81 429	94.90 379	96.49 428	88.87 381	97.31 125	94.62 453	85.92 483	90.50 509	96.84 358	85.05 424	99.40 284	83.77 507	95.78 505	96.43 479

GA-MVS		92.83 416	92.15 422	94.87 381	96.97 411	87.27 432	90.03 506	96.12 417	91.83 375	94.05 443	94.57 461	76.01 488	98.97 402	92.46 356	97.34 458	98.36 360

miper_lstm_enhance		94.81 333	94.80 327	94.85 382	96.16 445	86.45 444	91.14 486	98.20 305	93.49 314	97.03 288	97.37 312	84.97 426	99.26 345	95.28 222	99.56 159	98.83 288

IterMVS-SCA-FT		95.86 269	96.19 251	94.85 382	97.68 355	85.53 459	92.42 447	97.63 366	96.99 111	98.36 145	98.54 135	87.94 380	99.75 85	97.07 107	99.08 308	99.27 178

c3_l		95.20 312	95.32 293	94.83 384	96.19 442	86.43 445	91.83 465	98.35 290	93.47 315	97.36 259	97.26 321	88.69 368	99.28 340	95.41 213	99.36 249	98.78 294

testgi		96.07 254	96.50 232	94.80 385	99.26 68	87.69 422	95.96 244	98.58 252	95.08 237	98.02 200	96.25 399	97.92 24	97.60 490	88.68 441	98.74 362	99.11 225

mvsany_test1		93.47 393	93.03 394	94.79 386	94.05 520	92.12 277	90.82 494	90.01 524	85.02 496	97.26 265	98.28 184	93.57 257	97.03 497	92.51 355	95.75 507	95.23 500

CR-MVSNet		93.29 403	92.79 403	94.78 387	95.44 482	88.15 406	96.18 216	97.20 379	84.94 498	94.10 440	98.57 130	77.67 476	99.39 293	95.17 232	95.81 501	96.81 465

IMVS_0403		96.27 243	96.77 206	94.76 388	97.83 323	86.11 451	96.00 236	98.82 199	94.48 270	97.49 248	97.14 329	95.38 188	99.40 284	95.00 249	98.78 352	98.78 294

eth_miper_zixun_eth		94.89 329	94.93 314	94.75 389	95.99 455	86.12 450	91.35 475	98.49 263	93.40 316	97.12 278	97.25 322	86.87 403	99.35 312	95.08 242	98.82 346	98.78 294

IMVS_0407		96.35 239	96.88 198	94.74 390	97.83 323	86.11 451	96.25 211	98.82 199	94.48 270	97.57 241	97.14 329	96.08 152	99.33 317	95.00 249	98.78 352	98.78 294

MVS_Test		96.27 243	96.79 205	94.73 391	96.94 414	86.63 442	96.18 216	98.33 291	94.94 247	96.07 364	98.28 184	95.25 196	99.26 345	97.21 96	97.90 424	98.30 368

SP-MNN		94.33 360	94.22 358	94.67 392	94.94 501	92.73 256	93.74 399	96.59 413	92.73 352	93.75 452	95.38 445	88.24 376	95.08 517	94.86 264	97.78 429	96.20 484

SD_0403		93.73 382	93.43 383	94.64 393	97.85 313	86.35 447	97.47 115	97.94 335	93.50 313	93.71 454	96.73 367	93.77 252	98.84 414	73.48 537	96.39 486	98.72 310

miper_ehance_all_eth		94.69 339	94.70 330	94.64 393	95.77 470	86.22 448	91.32 478	98.24 300	91.67 379	97.05 287	96.65 372	88.39 373	99.22 356	94.88 260	98.34 399	98.49 343

Patchmatch-test		93.60 390	93.25 387	94.63 395	96.14 449	87.47 425	96.04 231	94.50 455	93.57 309	96.47 337	96.97 348	76.50 484	98.61 443	90.67 405	98.41 397	97.81 417

baseline1		93.14 407	92.64 410	94.62 396	97.34 394	87.20 433	96.67 176	93.02 480	94.71 259	96.51 334	95.83 427	81.64 452	98.60 445	90.00 419	88.06 536	98.07 391

xiu_mvs_v1_base_debu		95.62 287	95.96 265	94.60 397	98.01 296	88.42 393	93.99 386	98.21 302	92.98 341	95.91 372	94.53 463	96.39 134	99.72 111	95.43 210	98.19 405	95.64 494

xiu_mvs_v1_base		95.62 287	95.96 265	94.60 397	98.01 296	88.42 393	93.99 386	98.21 302	92.98 341	95.91 372	94.53 463	96.39 134	99.72 111	95.43 210	98.19 405	95.64 494

xiu_mvs_v1_base_debi		95.62 287	95.96 265	94.60 397	98.01 296	88.42 393	93.99 386	98.21 302	92.98 341	95.91 372	94.53 463	96.39 134	99.72 111	95.43 210	98.19 405	95.64 494

MS-PatchMatch		94.83 331	94.91 316	94.57 400	96.81 417	87.10 435	94.23 369	97.34 374	88.74 449	97.14 276	97.11 336	91.94 310	98.23 473	92.99 345	97.92 420	98.37 355

IMVS_0404		95.66 285	96.03 259	94.55 401	97.83 323	86.11 451	93.24 423	98.82 199	94.48 270	95.51 398	97.14 329	93.49 259	98.78 420	95.00 249	98.78 352	98.78 294

USDC		94.56 350	94.57 342	94.55 401	97.78 341	86.43 445	92.75 434	98.65 243	85.96 482	96.91 302	97.93 242	90.82 326	98.74 425	90.71 402	99.59 144	98.47 344

BH-untuned		94.69 339	94.75 329	94.52 403	97.95 306	87.53 424	94.07 381	97.01 394	93.99 295	97.10 280	95.65 433	92.65 286	98.95 403	87.60 456	96.74 475	97.09 451

dmvs_re		92.08 438	91.27 443	94.51 404	97.16 404	92.79 253	95.65 270	92.64 487	94.11 289	92.74 484	90.98 514	83.41 442	94.44 527	80.72 519	94.07 521	96.29 482

dcpmvs_2		97.12 174	97.99 78	94.51 404	99.11 105	84.00 488	97.75 87	99.65 13	97.38 96	99.14 49	98.42 151	95.16 201	99.96 2	95.52 197	99.78 69	99.58 51

VortexMVS		96.04 257	96.56 222	94.49 406	97.60 369	84.36 483	96.05 229	98.67 235	94.74 254	98.95 70	98.78 94	87.13 398	99.50 231	97.37 92	99.76 72	99.60 47

SIFT-ConvMatch		93.72 383	93.47 381	94.48 407	96.22 441	96.63 63	90.58 498	93.91 463	91.70 377	97.70 233	96.17 403	89.03 363	95.12 515	86.29 473	99.65 113	91.69 524

cl22		93.25 404	92.84 402	94.46 408	94.30 513	86.00 455	91.09 489	96.64 411	90.74 410	95.79 383	96.31 394	78.24 473	98.77 422	94.15 297	98.34 399	98.62 322

MDA-MVSNet_test_wron		94.73 334	94.83 325	94.42 409	97.48 381	85.15 468	90.28 503	95.87 425	92.52 355	97.48 251	97.76 264	91.92 311	99.17 368	93.32 335	96.80 473	98.94 266

YYNet1		94.73 334	94.84 323	94.41 410	97.47 385	85.09 470	90.29 502	95.85 426	92.52 355	97.53 244	97.76 264	91.97 308	99.18 363	93.31 336	96.86 468	98.95 263

icg_test_0407_2		95.88 267	96.39 238	94.36 411	97.83 323	86.11 451	91.82 466	98.82 199	94.48 270	97.57 241	97.14 329	96.08 152	98.20 476	95.00 249	98.78 352	98.78 294

ADS-MVSNet2		91.47 449	90.51 459	94.36 411	95.51 480	85.63 457	95.05 326	95.70 427	83.46 507	92.69 485	96.84 358	79.15 470	99.41 282	85.66 483	90.52 530	98.04 399

test_cas_vis1_n_1920		95.34 305	95.67 283	94.35 413	98.21 270	86.83 440	95.61 276	99.26 48	90.45 416	98.17 179	98.96 74	84.43 431	98.31 469	96.74 119	99.17 294	97.90 409

RRT-MVS		95.78 272	96.25 247	94.35 413	96.68 421	84.47 481	97.72 95	99.11 84	97.23 105	97.27 263	98.72 102	86.39 410	99.79 53	95.49 198	97.67 441	98.80 291

new_pmnet		92.34 428	91.69 436	94.32 415	96.23 439	89.16 370	92.27 453	92.88 482	84.39 504	95.29 405	96.35 391	85.66 418	96.74 505	84.53 498	97.56 447	97.05 452

MG-MVS		94.08 370	94.00 366	94.32 415	97.09 408	85.89 456	93.19 426	95.96 422	92.52 355	94.93 416	97.51 294	89.54 349	98.77 422	87.52 460	97.71 437	98.31 365

PatchT		93.75 379	93.57 378	94.29 417	95.05 495	87.32 431	96.05 229	92.98 481	97.54 82	94.25 432	98.72 102	75.79 490	99.24 352	95.92 170	95.81 501	96.32 481

test_fmvs1		94.51 353	94.60 337	94.26 418	95.91 458	87.92 412	95.35 297	99.02 122	86.56 478	96.79 308	98.52 136	82.64 447	97.00 499	97.87 65	98.71 366	97.88 411

miper_enhance_ethall		93.14 407	92.78 405	94.20 419	93.65 523	85.29 465	89.97 507	97.85 343	85.05 494	96.15 363	94.56 462	85.74 415	99.14 371	93.74 319	98.34 399	98.17 385

IterMVS		95.42 299	95.83 277	94.20 419	97.52 377	83.78 491	92.41 448	97.47 371	95.49 217	98.06 194	98.49 140	87.94 380	99.58 204	96.02 162	99.02 315	99.23 190
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thisisatest0515		90.43 460	89.18 474	94.17 421	97.07 409	85.44 460	89.75 516	87.58 534	88.28 457	93.69 457	91.72 506	65.27 520	99.58 204	90.59 406	98.67 371	97.50 439

testing3		89.72 472	88.26 482	94.10 422	97.66 360	84.30 486	94.80 340	88.25 530	94.66 260	95.07 409	92.51 497	41.15 552	99.43 268	91.81 371	98.44 395	98.55 331

SIFT-NN-CMatch		92.54 423	92.03 424	94.07 423	96.08 451	96.27 84	89.47 520	90.90 509	90.26 424	92.89 478	94.83 458	90.17 341	94.95 519	84.92 495	98.78 352	90.99 531

MatchFormer		93.37 397	93.14 390	94.07 423	96.06 454	92.91 247	94.24 367	94.92 449	85.51 487	98.29 158	97.79 261	85.70 417	96.13 509	86.23 474	99.51 189	93.18 517

SIFT-NCM-Cal		93.81 377	93.73 372	94.05 425	96.55 424	96.75 55	91.23 482	93.80 464	91.44 397	95.86 380	96.27 396	90.82 326	93.76 530	88.26 449	99.37 244	91.63 525

ECVR-MVS		94.37 359	94.48 345	94.05 425	98.95 134	83.10 494	98.31 43	82.48 544	96.20 159	98.23 171	99.16 49	81.18 457	99.66 169	95.95 167	99.83 55	99.38 143

SIFT-PointCN		93.04 411	92.72 407	94.01 427	95.80 467	95.33 146	89.76 514	92.60 489	90.24 425	96.32 344	95.87 425	87.45 390	94.70 524	86.65 471	99.77 71	92.01 520

SIFT-CM-Cal		93.31 400	93.10 391	93.95 428	96.19 442	96.32 79	89.81 512	93.40 474	91.16 403	97.19 272	96.07 415	88.24 376	94.58 525	86.11 475	99.69 99	90.94 532

SIFT-NN-NCMNet		92.32 430	91.79 431	93.89 429	96.32 434	96.91 50	90.32 501	90.69 516	90.36 420	91.72 500	95.43 444	88.98 364	94.27 529	84.23 499	98.06 412	90.49 537

test_vis1_n_1920		95.77 273	96.41 237	93.85 430	98.55 218	84.86 475	95.91 249	99.71 7	92.72 353	97.67 235	98.90 85	87.44 392	98.73 426	97.96 61	98.85 340	97.96 405

thres600view7		92.03 440	91.43 438	93.82 431	98.19 273	84.61 479	96.27 207	90.39 517	96.81 124	96.37 342	93.11 479	73.44 504	99.49 237	80.32 520	97.95 419	97.36 442

FPMVS		89.92 468	88.63 477	93.82 431	98.37 250	96.94 49	91.58 470	93.34 475	88.00 462	90.32 511	97.10 337	70.87 511	91.13 542	71.91 540	96.16 495	93.39 516

SIFT-MNN		93.13 409	92.91 398	93.79 433	96.42 430	96.49 68	91.23 482	93.73 465	92.18 365	95.52 397	96.08 414	84.66 429	93.04 537	87.49 461	98.94 324	91.84 521

SIFT-UM-Cal		93.74 380	93.73 372	93.78 434	95.97 457	96.07 94	89.78 513	96.67 410	91.69 378	97.77 231	96.09 413	89.51 353	94.75 521	86.68 470	99.39 240	90.52 536

ttmdpeth		94.05 371	94.15 362	93.75 435	95.81 466	85.32 463	96.00 236	94.93 448	92.07 368	94.19 435	99.09 58	85.73 416	96.41 507	90.98 387	98.52 384	99.53 78

test1111		94.53 352	94.81 326	93.72 436	99.06 113	81.94 504	98.31 43	83.87 542	96.37 148	98.49 126	99.17 48	81.49 453	99.73 101	96.64 122	99.86 35	99.49 96

thres400		91.68 446	91.00 447	93.71 437	98.02 294	84.35 484	95.70 263	90.79 511	96.26 153	95.90 376	92.13 502	73.62 501	99.42 272	78.85 526	97.74 434	97.36 442

IB-MVS	85.98 20	88.63 485	86.95 496	93.68 438	95.12 493	84.82 477	90.85 493	90.17 522	87.55 466	88.48 529	91.34 510	58.01 527	99.59 201	87.24 465	93.80 523	96.63 471
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
SIFT-UMatch		93.66 387	93.67 375	93.63 439	96.30 435	96.15 90	90.62 496	94.47 456	92.12 366	97.39 258	96.18 402	87.74 386	93.63 532	88.59 442	99.64 117	91.12 529

EU-MVSNet		94.25 361	94.47 346	93.60 440	98.14 285	82.60 499	97.24 130	92.72 485	85.08 493	98.48 128	98.94 77	82.59 448	98.76 424	97.47 86	99.53 176	99.44 122

TR-MVS		92.54 423	92.20 420	93.57 441	96.49 428	86.66 441	93.51 413	94.73 451	89.96 430	94.95 414	93.87 474	90.24 340	98.61 443	81.18 518	94.88 514	95.45 498

cascas		91.89 442	91.35 440	93.51 442	94.27 514	85.60 458	88.86 524	98.61 245	79.32 528	92.16 494	91.44 509	89.22 361	98.12 477	90.80 395	97.47 453	96.82 464

ppachtmachnet_test		94.49 354	94.84 323	93.46 443	96.16 445	82.10 501	90.59 497	97.48 370	90.53 415	97.01 291	97.59 285	91.01 322	99.36 308	93.97 308	99.18 291	98.94 266

SP-NN		92.63 421	92.38 415	93.37 444	93.30 527	92.36 264	92.04 460	94.24 460	91.60 386	89.19 523	93.92 473	87.21 396	91.28 540	93.73 321	96.17 493	96.48 476

dtuonlycased		95.11 318	95.70 282	93.35 445	99.05 119	81.45 508	91.13 488	98.48 265	93.11 337	97.98 208	97.27 319	96.15 150	99.32 325	89.61 425	98.50 388	99.27 178

SIFT-NN-UMatch		92.28 432	91.93 426	93.34 446	96.13 450	96.04 96	90.05 505	92.08 493	90.41 417	92.88 479	95.29 446	87.36 395	93.63 532	85.33 488	97.87 426	90.34 538

SIFT-NCMNet		93.23 406	93.19 389	93.34 446	95.31 488	95.59 118	88.29 526	95.60 433	91.60 386	98.43 135	96.34 393	89.80 346	93.57 534	83.82 506	99.57 154	90.85 533

SSC-MVS3.2		95.75 276	96.56 222	93.34 446	98.69 192	80.75 514	91.60 469	97.43 373	97.37 97	96.99 293	97.02 342	93.69 255	99.71 127	96.32 144	99.89 26	99.55 71

SIFT-NN-PointCN		92.48 425	92.19 421	93.33 449	95.40 486	95.65 116	90.19 504	93.07 479	88.67 451	92.90 477	95.95 421	89.38 358	93.20 535	85.21 490	98.94 324	91.15 528

pmmvs3		90.00 465	88.90 476	93.32 450	94.20 517	85.34 462	91.25 481	92.56 490	78.59 532	93.82 448	95.17 449	67.36 519	98.69 433	89.08 434	98.03 414	95.92 486

EPNet_dtu		91.39 451	90.75 454	93.31 451	90.48 541	82.61 498	94.80 340	92.88 482	93.39 317	81.74 542	94.90 457	81.36 456	99.11 379	88.28 447	98.87 337	98.21 379
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres100view900		91.76 445	91.26 445	93.26 452	98.21 270	84.50 480	96.39 195	90.39 517	96.87 121	96.33 343	93.08 483	73.44 504	99.42 272	78.85 526	97.74 434	95.85 490

baseline2		89.65 474	88.44 480	93.25 453	95.62 477	82.71 496	93.82 395	85.94 539	88.89 447	87.35 534	92.54 496	71.23 509	99.33 317	86.01 477	94.60 518	97.72 425

DSMNet-mixed		92.19 434	91.83 428	93.25 453	96.18 444	83.68 492	96.27 207	93.68 469	76.97 538	92.54 491	99.18 45	89.20 362	98.55 449	83.88 504	98.60 380	97.51 437

SIFT-PCN-Cal		93.02 412	92.95 397	93.23 455	95.63 476	94.57 182	89.68 517	94.71 452	90.40 418	97.02 289	95.84 426	88.33 375	93.66 531	85.26 489	99.65 113	91.45 527

ETVMVS		87.62 495	85.75 502	93.22 456	96.15 448	83.26 493	92.94 430	90.37 519	91.39 398	90.37 510	88.45 528	51.93 547	98.64 440	73.76 535	96.38 487	97.75 421

MVStest1		91.89 442	91.45 437	93.21 457	89.01 543	84.87 474	95.82 257	95.05 446	91.50 391	98.75 96	99.19 41	57.56 528	95.11 516	97.78 71	98.37 398	99.64 44

tfpn200view9		91.55 447	91.00 447	93.21 457	98.02 294	84.35 484	95.70 263	90.79 511	96.26 153	95.90 376	92.13 502	73.62 501	99.42 272	78.85 526	97.74 434	95.85 490

mvs_anonymous		95.36 303	96.07 257	93.21 457	96.29 436	81.56 506	94.60 350	97.66 357	93.30 322	96.95 298	98.91 84	93.03 277	99.38 297	96.60 128	97.30 460	98.69 315

0.4-1-1-0.1		83.64 505	80.50 508	93.08 460	90.32 542	85.42 461	86.48 529	87.71 533	83.60 506	80.38 545	75.45 543	53.19 545	98.91 404	86.46 472	80.88 542	94.93 504

our_test_3		94.20 366	94.58 340	93.07 461	96.16 445	81.20 511	90.42 500	96.84 400	90.72 411	97.14 276	97.13 333	90.47 331	99.11 379	94.04 303	98.25 403	98.91 274

MASt3R-SfM		91.42 450	90.88 450	93.06 462	92.40 534	92.08 281	89.76 514	93.15 478	78.62 531	95.98 369	97.33 315	82.42 449	91.17 541	90.23 415	97.98 416	95.92 486

testing91		89.67 473	88.55 478	93.04 463	95.90 459	81.80 505	92.71 438	93.71 466	93.71 303	90.18 513	90.15 519	57.11 530	99.22 356	87.17 466	96.32 489	98.12 387

ADS-MVSNet		90.95 457	90.26 462	93.04 463	95.51 480	82.37 500	95.05 326	93.41 473	83.46 507	92.69 485	96.84 358	79.15 470	98.70 431	85.66 483	90.52 530	98.04 399

PAPM		87.64 494	85.84 501	93.04 463	96.54 425	84.99 472	88.42 525	95.57 434	79.52 526	83.82 539	93.05 485	80.57 461	98.41 461	62.29 543	92.79 525	95.71 493

PS-MVSNAJ		94.10 368	94.47 346	93.00 466	97.35 392	84.88 473	91.86 464	97.84 345	91.96 372	94.17 437	92.50 498	95.82 164	99.71 127	91.27 380	97.48 451	94.40 509

xiu_mvs_v2_base		94.22 362	94.63 335	92.99 467	97.32 397	84.84 476	92.12 457	97.84 345	91.96 372	94.17 437	93.43 477	96.07 154	99.71 127	91.27 380	97.48 451	94.42 508

SCA		93.38 396	93.52 380	92.96 468	96.24 437	81.40 509	93.24 423	94.00 462	91.58 389	94.57 425	96.97 348	87.94 380	99.42 272	89.47 428	97.66 444	98.06 395

new-patchmatchnet		95.67 283	96.58 219	92.94 469	97.48 381	80.21 517	92.96 429	98.19 311	94.83 252	98.82 86	98.79 91	93.31 265	99.51 230	95.83 178	99.04 314	99.12 220

PDCNetPlus		89.44 476	88.28 481	92.93 470	91.75 537	85.02 471	87.69 527	99.67 9	82.69 509	95.89 379	97.02 342	51.15 548	95.27 513	88.79 437	99.86 35	98.50 341

testing222		87.35 497	85.50 504	92.93 470	95.79 468	82.83 495	92.40 449	90.10 523	92.80 350	88.87 526	89.02 524	48.34 550	98.70 431	75.40 534	96.74 475	97.27 447

Syy-MVS		92.09 437	91.80 430	92.93 470	95.19 491	82.65 497	92.46 444	91.35 503	90.67 413	91.76 498	87.61 530	85.64 419	98.50 454	94.73 274	96.84 469	97.65 428

test0.0.03 1		90.11 462	89.21 471	92.83 473	93.89 521	86.87 439	91.74 467	88.74 528	92.02 370	94.71 423	91.14 512	73.92 498	94.48 526	83.75 508	92.94 524	97.16 448

testing11		88.93 480	87.63 490	92.80 474	95.87 461	81.49 507	92.48 443	91.54 501	91.62 381	88.27 530	90.24 517	55.12 542	99.11 379	87.30 464	96.28 491	97.81 417

thres200		91.00 456	90.42 460	92.77 475	97.47 385	83.98 489	94.01 385	91.18 507	95.12 236	95.44 400	91.21 511	73.93 497	99.31 327	77.76 530	97.63 446	95.01 501

BH-w/o		92.14 435	91.94 425	92.73 476	97.13 407	85.30 464	92.46 444	95.64 429	89.33 437	94.21 434	92.74 493	89.60 347	98.24 472	81.68 515	94.66 516	94.66 505

testing99		89.21 478	88.04 485	92.70 477	95.78 469	81.00 513	92.65 439	92.03 494	93.20 328	89.90 518	90.08 521	55.25 539	99.14 371	87.54 458	95.95 496	97.97 404

0.3-1-1-0.015		82.33 508	78.89 510	92.66 478	88.57 544	84.69 478	84.76 534	88.02 532	82.48 512	77.55 547	72.96 544	49.60 549	98.87 412	86.05 476	80.02 544	94.43 507

1314		92.38 427	92.30 417	92.64 479	95.42 484	85.15 468	95.86 253	96.97 396	85.40 491	90.62 506	93.06 484	91.12 319	97.80 487	86.74 468	95.49 510	94.97 503

SSC-MVS		95.92 265	97.03 184	92.58 480	99.28 64	78.39 522	96.68 174	95.12 445	98.90 25	99.11 51	98.66 115	91.36 317	99.68 151	95.00 249	99.16 295	99.67 36

KD-MVS_2432*1600		88.93 480	87.74 486	92.49 481	88.04 547	81.99 502	89.63 518	95.62 430	91.35 399	95.06 410	93.11 479	56.58 532	98.63 441	85.19 491	95.07 511	96.85 461

miper_refine_blended		88.93 480	87.74 486	92.49 481	88.04 547	81.99 502	89.63 518	95.62 430	91.35 399	95.06 410	93.11 479	56.58 532	98.63 441	85.19 491	95.07 511	96.85 461

MVS		90.02 464	89.20 472	92.47 483	94.71 506	86.90 438	95.86 253	96.74 406	64.72 543	90.62 506	92.77 492	92.54 293	98.39 463	79.30 523	95.56 509	92.12 519

PMMVS2		93.66 387	94.07 364	92.45 484	97.57 370	80.67 515	86.46 530	96.00 420	93.99 295	97.10 280	97.38 310	89.90 344	97.82 486	88.76 438	99.47 208	98.86 285

0.4-1-1-0.2		82.53 507	79.25 509	92.37 485	88.10 546	83.96 490	83.72 537	88.15 531	82.14 514	78.97 546	72.49 545	53.22 544	98.84 414	85.99 478	80.50 543	94.30 510

CHOSEN 280x420		89.98 466	89.19 473	92.37 485	95.60 478	81.13 512	86.22 531	97.09 388	81.44 519	87.44 533	93.15 478	73.99 496	99.47 246	88.69 440	99.07 310	96.52 474

PatchmatchNet		91.98 441	91.87 427	92.30 487	94.60 509	79.71 518	95.12 315	93.59 472	89.52 435	93.61 459	97.02 342	77.94 474	99.18 363	90.84 393	94.57 519	98.01 402
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WBMVS		91.11 453	90.72 455	92.26 488	95.99 455	77.98 527	91.47 472	95.90 424	91.63 380	95.90 376	96.45 384	59.60 525	99.46 253	89.97 420	99.59 144	99.33 158

gg-mvs-nofinetune		88.28 490	86.96 495	92.23 489	92.84 532	84.44 482	98.19 56	74.60 549	99.08 16	87.01 535	99.47 16	56.93 531	98.23 473	78.91 525	95.61 508	94.01 512

WB-MVSnew		91.50 448	91.29 441	92.14 490	94.85 503	80.32 516	93.29 422	88.77 527	88.57 453	94.03 444	92.21 500	92.56 289	98.28 471	80.21 521	97.08 462	97.81 417

WB-MVS		95.50 292	96.62 213	92.11 491	99.21 85	77.26 532	96.12 223	95.40 439	98.62 34	98.84 83	98.26 189	91.08 320	99.50 231	93.37 332	98.70 368	99.58 51

test2506		89.86 469	89.16 475	91.97 492	98.95 134	76.83 533	98.54 26	61.07 553	96.20 159	97.07 286	99.16 49	55.19 541	99.69 144	96.43 138	99.83 55	99.38 143

myMVS_eth3d		87.16 500	85.61 503	91.82 493	95.19 491	79.32 519	92.46 444	91.35 503	90.67 413	91.76 498	87.61 530	41.96 551	98.50 454	82.66 511	96.84 469	97.65 428

tpm		91.08 455	90.85 452	91.75 494	95.33 487	78.09 524	95.03 328	91.27 506	88.75 448	93.53 463	97.40 303	71.24 508	99.30 331	91.25 382	93.87 522	97.87 412

UBG		88.29 489	87.17 492	91.63 495	96.08 451	78.21 523	91.61 468	91.50 502	89.67 434	89.71 519	88.97 525	59.01 526	98.91 404	81.28 517	96.72 477	97.77 420

PVSNet	86.72 19	91.10 454	90.97 449	91.49 496	97.56 372	78.04 525	87.17 528	94.60 454	84.65 500	92.34 492	92.20 501	87.37 394	98.47 457	85.17 493	97.69 439	97.96 405

reproduce_monomvs		92.05 439	92.26 418	91.43 497	95.42 484	75.72 537	95.68 266	97.05 391	94.47 274	97.95 213	98.35 164	55.58 538	99.05 388	96.36 141	99.44 217	99.51 85

SIFT-NN		89.78 470	89.23 469	91.41 498	95.04 496	94.89 167	88.98 523	90.76 513	89.26 440	89.11 525	92.97 486	81.45 454	88.25 543	78.47 529	97.06 463	91.08 530

EPMVS		89.26 477	88.55 478	91.39 499	92.36 535	79.11 521	95.65 270	79.86 545	88.60 452	93.12 473	96.53 379	70.73 512	98.10 478	90.75 398	89.32 534	96.98 454

MonoMVSNet		93.30 402	93.96 369	91.33 500	94.14 518	81.33 510	97.68 98	96.69 408	95.38 225	96.32 344	98.42 151	84.12 434	96.76 504	90.78 396	92.12 528	95.89 488

CostFormer		89.75 471	89.25 468	91.26 501	94.69 507	78.00 526	95.32 301	91.98 496	81.50 518	90.55 508	96.96 350	71.06 510	98.89 407	88.59 442	92.63 526	96.87 459

CVMVSNet		92.33 429	92.79 403	90.95 502	97.26 399	75.84 536	95.29 305	92.33 492	81.86 515	96.27 351	98.19 199	81.44 455	98.46 459	94.23 294	98.29 402	98.55 331

XFeat-MNN		88.85 483	88.16 483	90.91 503	88.38 545	89.73 352	84.46 535	91.81 498	83.72 505	95.56 395	92.95 487	74.60 495	92.68 538	84.01 501	97.99 415	90.32 539

tpm2		88.47 486	87.69 489	90.79 504	94.98 500	77.34 530	95.09 319	91.83 497	77.51 537	89.40 521	96.41 386	67.83 518	98.73 426	83.58 509	92.60 527	96.29 482

GG-mvs-BLEND				90.60 505	91.00 538	84.21 487	98.23 50	72.63 552		82.76 540	84.11 540	56.14 534	96.79 502	72.20 539	92.09 529	90.78 534

tpmvs		90.79 459	90.87 451	90.57 506	92.75 533	76.30 534	95.79 258	93.64 471	91.04 406	91.91 496	96.26 397	77.19 482	98.86 413	89.38 430	89.85 533	96.56 473

test-LLR		89.97 467	89.90 464	90.16 507	94.24 515	74.98 538	89.89 508	89.06 525	92.02 370	89.97 516	90.77 515	73.92 498	98.57 446	91.88 366	97.36 456	96.92 456

test-mter		87.92 493	87.17 492	90.16 507	94.24 515	74.98 538	89.89 508	89.06 525	86.44 479	89.97 516	90.77 515	54.96 543	98.57 446	91.88 366	97.36 456	96.92 456

UWE-MVS		87.57 496	86.72 497	90.13 509	95.21 490	73.56 543	91.94 462	83.78 543	88.73 450	93.00 475	92.87 490	55.22 540	99.25 348	81.74 514	97.96 418	97.59 434

myMVS_eth3d28		88.32 488	87.73 488	90.11 510	96.42 430	74.96 541	92.21 454	92.37 491	93.56 310	90.14 514	89.61 522	56.13 535	98.05 480	81.84 513	97.26 461	97.33 445

tpm cat1		88.01 492	87.33 491	90.05 511	94.48 510	76.28 535	94.47 355	94.35 458	73.84 542	89.26 522	95.61 436	73.64 500	98.30 470	84.13 500	86.20 538	95.57 497

tpmrst		90.31 461	90.61 458	89.41 512	94.06 519	72.37 546	95.06 325	93.69 467	88.01 461	92.32 493	96.86 356	77.45 478	98.82 416	91.04 385	87.01 537	97.04 453

testing3-2		90.09 463	90.38 461	89.24 513	98.07 290	69.88 549	95.12 315	90.71 515	96.65 129	93.60 461	94.03 471	55.81 537	99.33 317	90.69 404	98.71 366	98.51 338

TESTMET0.1,1		87.20 499	86.57 498	89.07 514	93.62 524	72.84 545	89.89 508	87.01 537	85.46 490	89.12 524	90.20 518	56.00 536	97.72 488	90.91 390	96.92 465	96.64 469

dtuonly		92.30 431	93.44 382	88.89 515	95.60 478	69.49 550	89.18 521	98.09 324	88.17 459	94.19 435	96.35 391	88.98 364	98.72 429	91.74 375	98.69 369	98.45 347

E-PMN		89.52 475	89.78 465	88.73 516	93.14 528	77.61 528	83.26 539	92.02 495	94.82 253	93.71 454	93.11 479	75.31 491	96.81 501	85.81 480	96.81 472	91.77 523

EMVS		89.06 479	89.22 470	88.61 517	93.00 530	77.34 530	82.91 540	90.92 508	94.64 262	92.63 489	91.81 505	76.30 486	97.02 498	83.83 505	96.90 467	91.48 526

PVSNet_0	81.89 21	84.49 502	83.21 506	88.34 518	95.76 471	74.97 540	83.49 538	92.70 486	78.47 533	87.94 531	86.90 538	83.38 443	96.63 506	73.44 538	66.86 547	93.40 515

dmvs_testset		87.30 498	86.99 494	88.24 519	96.71 420	77.48 529	94.68 347	86.81 538	92.64 354	89.61 520	87.01 536	85.91 414	93.12 536	61.04 544	88.49 535	94.13 511

MVE	73.61 22	86.48 501	85.92 500	88.18 520	96.23 439	85.28 466	81.78 541	75.79 548	86.01 481	82.53 541	91.88 504	92.74 282	87.47 545	71.42 541	94.86 515	91.78 522
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dp		88.08 491	88.05 484	88.16 521	92.85 531	68.81 551	94.17 373	92.88 482	85.47 489	91.38 502	96.14 408	68.87 517	98.81 418	86.88 467	83.80 540	96.87 459

UWE-MVS-28		83.78 504	82.36 507	88.03 522	90.72 540	71.58 547	93.64 406	77.87 546	87.62 465	85.91 538	92.89 489	59.94 524	95.99 511	56.06 546	96.56 483	96.52 474

wuyk23d		93.25 404	95.20 296	87.40 523	96.07 453	95.38 134	97.04 142	94.97 447	95.33 226	99.70 9	98.11 212	98.14 21	91.94 539	77.76 530	99.68 104	74.89 543

XFeat-NN		84.28 503	83.52 505	86.54 524	85.42 550	86.22 448	78.86 542	88.43 529	79.17 529	90.71 505	89.11 523	69.18 516	85.27 547	76.68 532	94.13 520	88.13 540

MVS-HIRNet		88.40 487	90.20 463	82.99 525	97.01 410	60.04 552	93.11 428	85.61 540	84.45 503	88.72 527	99.09 58	84.72 428	98.23 473	82.52 512	96.59 482	90.69 535

GLUNet-SfM		74.13 509	71.69 512	81.46 526	63.16 553	74.17 542	66.80 543	76.03 547	58.10 545	88.60 528	86.99 537	57.56 528	86.25 546	50.03 547	97.91 423	83.95 541

DeepMVS_CX				77.17 527	90.94 539	85.28 466		74.08 551	52.51 546	80.87 544	88.03 529	75.25 492	70.63 549	59.23 545	84.94 539	75.62 542

test_method		66.88 510	66.13 513	69.11 528	62.68 554	25.73 557	49.76 544	96.04 419	14.32 548	64.27 549	91.69 507	73.45 503	88.05 544	76.06 533	66.94 546	93.54 513

dongtai		63.43 511	63.37 514	63.60 529	83.91 551	53.17 554	85.14 532	43.40 556	77.91 536	80.96 543	79.17 542	36.36 553	77.10 548	37.88 548	45.63 548	60.54 544

kuosan		54.81 513	54.94 516	54.42 530	74.43 552	50.03 555	84.98 533	44.27 555	61.80 544	62.49 550	70.43 546	35.16 554	58.04 550	19.30 549	41.61 549	55.19 545

tmp_tt		57.23 512	62.50 515	41.44 531	34.77 555	49.21 556	83.93 536	60.22 554	15.31 547	71.11 548	79.37 541	70.09 514	44.86 551	64.76 542	82.93 541	30.25 546

test123		12.59 515	15.49 518	3.87 532	6.07 556	2.55 558	90.75 495	2.59 558	2.52 550	5.20 553	13.02 549	4.96 555	1.85 553	5.20 550	9.09 550	7.23 547

testmvs		12.33 516	15.23 519	3.64 533	5.77 557	2.23 559	88.99 522	3.62 557	2.30 551	5.29 552	13.09 548	4.52 556	1.95 552	5.16 551	8.32 551	6.75 548

mmdepth		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

monomultidepth		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

test_blank		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uanet_test		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

DCPMVS		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

cdsmvs_eth3d_5k		24.22 514	32.30 517	0.00 534	0.00 558	0.00 560	0.00 545	98.10 323	0.00 552	0.00 554	95.06 452	97.54 45	0.00 554	0.00 552	0.00 552	0.00 549

pcd_1.5k_mvsjas		7.98 517	10.65 520	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	95.82 164	0.00 554	0.00 552	0.00 552	0.00 549

sosnet-low-res		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

sosnet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uncertanet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

Regformer		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

ab-mvs-re		7.91 518	10.55 521	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	94.94 454	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uanet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

test-260524					98.88 150	95.35 137		98.76 216		98.18 178		95.58 179	99.73 101	96.66 121	99.51 189

WAC-MVS						79.32 519								85.41 486

FOURS1					99.59 18	98.20 7	99.03 8	99.25 50	98.96 24	98.87 79

PC_three_1452									87.24 469	98.37 142	97.44 300	97.00 83	96.78 503	92.01 362	99.25 281	99.21 194

test_one_0601					99.05 119	95.50 127		98.87 170	97.21 107	98.03 198	98.30 178	96.93 90

eth-test2					0.00 558

eth-test					0.00 558

ZD-MVS					98.43 243	95.94 102		98.56 255	90.72 411	96.66 321	97.07 338	95.02 207	99.74 95	91.08 384	98.93 329

RE-MVS-def			97.88 94		98.81 163	98.05 9	97.55 108	98.86 174	97.77 67	98.20 173	98.07 218	96.94 88		95.49 198	99.20 286	99.26 180

IU-MVS					99.22 78	95.40 132		98.14 319	85.77 486	98.36 145				95.23 226	99.51 189	99.49 96

test_241102_TWO								98.83 191	96.11 169	98.62 109	98.24 191	96.92 93	99.72 111	95.44 207	99.49 200	99.49 96

test_241102_ONE					99.22 78	95.35 137		98.83 191	96.04 178	99.08 54	98.13 207	97.87 28	99.33 317

9.14			96.69 209		98.53 221		96.02 234	98.98 142	93.23 324	97.18 273	97.46 298	96.47 128	99.62 188	92.99 345	99.32 267

save fliter					98.48 234	94.71 171	94.53 354	98.41 278	95.02 242

test_0728_THIRD									96.62 130	98.40 139	98.28 184	97.10 71	99.71 127	95.70 181	99.62 123	99.58 51

test0726					99.24 72	95.51 124	96.89 152	98.89 161	95.92 189	98.64 107	98.31 172	97.06 76

GSMVS																98.06 395

test_part2					99.03 122	96.07 94				98.08 191

sam_mvs1												77.80 475				98.06 395

sam_mvs												77.38 479

MTGPA								98.73 220

test_post1							94.98 330				10.37 551	76.21 487	99.04 390	89.47 428

test_post											10.87 550	76.83 483	99.07 386

patchmatchnet-post											96.84 358	77.36 480	99.42 272

MTMP							96.55 180	74.60 549

gm-plane-assit					91.79 536	71.40 548			81.67 516		90.11 520		98.99 396	84.86 496

test9_res														91.29 379	98.89 336	99.00 248

TEST9					97.84 320	95.23 149	93.62 407	98.39 282	86.81 475	93.78 449	95.99 417	94.68 218	99.52 226

test_8					97.81 329	95.07 161	93.54 412	98.38 284	87.04 471	93.71 454	95.96 420	94.58 223	99.52 226

agg_prior2														90.34 414	98.90 332	99.10 230

agg_prior					97.80 333	94.96 164		98.36 287		93.49 464			99.53 223

test_prior4						95.38 134	93.61 409

test_prior2							93.33 421		94.21 283	94.02 445	96.25 399	93.64 256		91.90 365	98.96 321

旧先验2							93.35 420		77.95 535	95.77 387			98.67 437	90.74 401

新几何2							93.43 415

旧先验1					97.80 333	93.87 211		97.75 351			97.04 341	93.57 257			98.68 370	98.72 310

无先验							93.20 425	97.91 338	80.78 521				99.40 284	87.71 453		97.94 407

原ACMM2							92.82 432

test222					98.17 279	93.24 239	92.74 436	97.61 367	75.17 539	94.65 424	96.69 370	90.96 325			98.66 373	97.66 427

testdata2													99.46 253	87.84 451

segment_acmp												95.34 190

testdata1							92.77 433		93.78 301

plane_prior7					98.70 189	94.67 174

plane_prior6					98.38 249	94.37 191						91.91 312

plane_prior5								98.75 217					99.46 253	92.59 352	99.20 286	99.28 174

plane_prior4											96.77 364

plane_prior3						94.51 184			95.29 229	96.16 360

plane_prior2							96.50 183		96.36 149

plane_prior1					98.49 232

plane_prior						94.29 195	95.42 287		94.31 281						98.93 329

n2								0.00 559

nn								0.00 559

door-mid								98.17 312

test11								98.08 326

door								97.81 349

HQP5-MVS						92.47 262

HQP-NCC					97.85 313		94.26 362		93.18 330	92.86 481

ACMP_Plane					97.85 313		94.26 362		93.18 330	92.86 481

BP-MVS														90.51 409

HQP4-MVS										92.87 480			99.23 354			99.06 238

HQP3-MVS								98.43 274							98.74 362

HQP2-MVS												90.33 335

NP-MVS					98.14 285	93.72 217					95.08 450

MDTV_nov1_ep13_2view						57.28 553	94.89 334		80.59 522	94.02 445		78.66 472		85.50 485		97.82 415

MDTV_nov1_ep13			91.28 442		94.31 512	73.51 544	94.80 340	93.16 477	86.75 477	93.45 466	97.40 303	76.37 485	98.55 449	88.85 436	96.43 484

ACMMP++_ref															99.52 183

ACMMP++															99.55 166

Test By Simon												94.51 227

High-resolution multi-view benchmark