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.

CHOSEN 1792x2688		97.12 89	96.80 84	98.08 103	99.30 55	94.56 213	98.05 214	99.71 1	93.57 177	97.09 104	98.91 79	88.17 183	99.89 29	96.87 78	99.56 64	99.81 2

HyFIR lowres test		96.90 97	96.49 100	98.14 97	99.33 45	95.56 147	97.38 268	99.65 2	92.34 229	97.61 93	98.20 142	89.29 141	99.10 169	96.97 66	97.60 147	99.77 14

MVS_111021_LR		98.34 38	98.23 34	98.67 66	99.27 64	96.90 83	97.95 224	99.58 3	97.14 33	98.44 52	99.01 65	95.03 54	99.62 108	97.91 30	99.75 32	99.50 74

MVS_111021_HR		98.47 30	98.34 22	98.88 58	99.22 74	97.32 67	97.91 229	99.58 3	97.20 29	98.33 56	99.00 66	95.99 27	99.64 103	98.05 27	99.76 26	99.69 38

PGM-MVS		98.49 29	98.23 34	99.27 25	99.72 11	98.08 41	98.99 63	99.49 5	95.43 88	99.03 18	99.32 21	95.56 38	99.94 3	96.80 80	99.77 20	99.78 7

ACMMP		98.23 43	97.95 43	99.09 44	99.74 7	97.62 58	99.03 60	99.41 6	95.98 69	97.60 94	99.36 17	94.45 67	99.93 9	97.14 62	98.85 100	99.70 37
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
CSCG		97.85 54	97.74 48	98.20 94	99.67 18	95.16 161	99.22 28	99.32 7	93.04 199	97.02 110	98.92 78	95.36 44	99.91 24	97.43 55	99.64 48	99.52 69

PVSNet_BlendedMVS		96.73 102	96.60 95	97.12 165	99.25 67	95.35 156	98.26 190	99.26 8	94.28 139	97.94 74	97.46 196	92.74 86	99.81 53	96.88 75	93.32 233	96.20 291

PVSNet_Blended		97.38 78	97.12 72	98.14 97	99.25 67	95.35 156	97.28 278	99.26 8	93.13 197	97.94 74	98.21 141	92.74 86	99.81 53	96.88 75	99.40 81	99.27 101

UniMVSNet_NR-MVSNet		95.71 138	95.15 146	97.40 153	96.84 232	96.97 79	98.74 119	99.24 10	95.16 108	93.88 225	97.72 180	91.68 106	98.31 265	95.81 110	87.25 303	96.92 219

WR-MVS_H		95.05 183	94.46 185	96.81 182	96.86 231	95.82 139	99.24 20	99.24 10	93.87 156	92.53 261	96.84 261	90.37 128	98.24 271	93.24 178	87.93 293	96.38 285

FC-MVSNet-test		96.42 113	96.05 112	97.53 141	96.95 224	97.27 69	99.36 8	99.23 12	95.83 73	93.93 223	98.37 124	92.00 101	98.32 263	96.02 104	92.72 241	97.00 214

VPA-MVSNet		95.75 135	95.11 147	97.69 127	97.24 207	97.27 69	98.94 71	99.23 12	95.13 109	95.51 166	97.32 207	85.73 240	98.91 192	97.33 59	89.55 270	96.89 227

FIs		96.51 110	96.12 111	97.67 129	97.13 217	97.54 61	99.36 8	99.22 14	95.89 71	94.03 221	98.35 126	91.98 102	98.44 244	96.40 94	92.76 240	97.01 213

tfpnnormal		93.66 251	92.70 257	96.55 213	96.94 225	95.94 122	98.97 67	99.19 15	91.04 266	91.38 278	97.34 205	84.94 253	98.61 216	85.45 313	89.02 278	95.11 311

UniMVSNet (Re)		95.78 134	95.19 145	97.58 138	96.99 223	97.47 63	98.79 108	99.18 16	95.60 81	93.92 224	97.04 237	91.68 106	98.48 234	95.80 112	87.66 298	96.79 237

PVSNet_Blended_VisFu		97.70 59	97.46 60	98.44 82	99.27 64	95.91 134	98.63 140	99.16 17	94.48 136	97.67 89	98.88 80	92.80 85	99.91 24	97.11 63	99.12 90	99.50 74

CHOSEN 280x420		97.18 86	97.18 71	97.20 159	98.81 114	93.27 246	95.78 322	99.15 18	95.25 104	96.79 126	98.11 147	92.29 91	99.07 172	98.56 9	99.85 2	99.25 103

PHI-MVS		98.34 38	98.06 39	99.18 34	99.15 81	98.12 40	99.04 59	99.09 19	93.32 191	98.83 32	99.10 51	96.54 11	99.83 46	97.70 44	99.76 26	99.59 64

UA-Net		97.96 47	97.62 50	98.98 51	98.86 110	97.47 63	98.89 78	99.08 20	96.67 49	98.72 38	99.54 1	93.15 82	99.81 53	94.87 137	98.83 101	99.65 53

PatchMatch-RL		96.59 107	96.03 114	98.27 90	99.31 50	96.51 98	97.91 229	99.06 21	93.72 166	96.92 116	98.06 150	88.50 178	99.65 101	91.77 222	99.00 93	98.66 150

3Dnovator	94.51 5	97.46 69	96.93 80	99.07 45	97.78 174	97.64 56	99.35 10	99.06 21	97.02 39	93.75 230	99.16 45	89.25 142	99.92 15	97.22 60	99.75 32	99.64 56

MSLP-MVS++		98.56 23	98.57 5	98.55 73	99.26 66	96.80 86	98.71 125	99.05 23	97.28 21	98.84 30	99.28 28	96.47 12	99.40 135	98.52 14	99.70 40	99.47 80

PS-CasMVS		94.67 210	93.99 212	96.71 186	96.68 241	95.26 159	99.13 47	99.03 24	93.68 172	92.33 267	97.95 158	85.35 247	98.10 276	93.59 171	88.16 292	96.79 237

TranMVSNet+NR-MVSNet		95.14 181	94.48 183	97.11 166	96.45 251	96.36 104	99.03 60	99.03 24	95.04 114	93.58 232	97.93 160	88.27 181	98.03 281	94.13 157	86.90 308	96.95 218

PEN-MVS		94.42 223	93.73 229	96.49 217	96.28 271	94.84 189	99.17 35	99.00 26	93.51 178	92.23 269	97.83 171	86.10 234	97.90 288	92.55 202	86.92 307	96.74 242

Vis-MVSNet		97.42 75	97.11 73	98.34 88	98.66 125	96.23 109	99.22 28	99.00 26	96.63 51	98.04 65	99.21 35	88.05 189	99.35 140	96.01 105	99.21 87	99.45 86
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DU-MVS		95.42 162	94.76 172	97.40 153	96.53 246	96.97 79	98.66 138	98.99 28	95.43 88	93.88 225	97.69 181	88.57 173	98.31 265	95.81 110	87.25 303	96.92 219

VPNet		94.99 185	94.19 198	97.40 153	97.16 215	96.57 95	98.71 125	98.97 29	95.67 78	94.84 176	98.24 140	80.36 300	98.67 213	96.46 91	87.32 301	96.96 216

OpenMVS	93.04 13	95.83 132	95.00 151	98.32 89	97.18 214	97.32 67	99.21 31	98.97 29	89.96 282	91.14 280	99.05 60	86.64 219	99.92 15	93.38 174	99.47 72	97.73 188

HFP-MVS		98.63 14	98.40 14	99.32 18	99.72 11	98.29 28	99.23 22	98.96 31	96.10 67	98.94 24	99.17 42	96.06 23	99.92 15	97.62 46	99.78 15	99.75 23

#test#		98.54 26	98.27 29	99.32 18	99.72 11	98.29 28	98.98 66	98.96 31	95.65 80	98.94 24	99.17 42	96.06 23	99.92 15	97.21 61	99.78 15	99.75 23

ACMMPR		98.59 18	98.36 19	99.29 20	99.74 7	98.15 38	99.23 22	98.95 33	96.10 67	98.93 28	99.19 41	95.70 36	99.94 3	97.62 46	99.79 11	99.78 7

CP-MVSNet		94.94 191	94.30 192	96.83 181	96.72 239	95.56 147	99.11 50	98.95 33	93.89 154	92.42 266	97.90 162	87.19 210	98.12 275	94.32 152	88.21 290	96.82 236

NR-MVSNet		94.98 187	94.16 199	97.44 149	96.53 246	97.22 73	98.74 119	98.95 33	94.96 118	89.25 297	97.69 181	89.32 140	98.18 273	94.59 145	87.40 300	96.92 219

region2R		98.61 15	98.38 17	99.29 20	99.74 7	98.16 37	99.23 22	98.93 36	96.15 62	98.94 24	99.17 42	95.91 31	99.94 3	97.55 51	99.79 11	99.78 7

APDe-MVS		99.02 1	98.84 1	99.55 3	99.57 25	98.96 5	99.39 5	98.93 36	97.38 17	99.41 4	99.54 1	96.66 8	99.84 45	98.86 2	99.85 2	99.87 1

VNet		97.79 56	97.40 63	98.96 53	98.88 108	97.55 60	98.63 140	98.93 36	96.74 46	99.02 19	98.84 83	90.33 130	99.83 46	98.53 10	96.66 159	99.50 74

UGNet		96.78 101	96.30 105	98.19 96	98.24 145	95.89 136	98.88 80	98.93 36	97.39 16	96.81 124	97.84 168	82.60 285	99.90 27	96.53 89	99.49 70	98.79 142
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
sss		97.39 77	96.98 79	98.61 69	98.60 131	96.61 94	98.22 192	98.93 36	93.97 151	98.01 69	98.48 115	91.98 102	99.85 43	96.45 92	98.15 129	99.39 89

QAPM		96.29 118	95.40 131	98.96 53	97.85 171	97.60 59	99.23 22	98.93 36	89.76 289	93.11 248	99.02 61	89.11 146	99.93 9	91.99 215	99.62 50	99.34 91

114514_t		96.93 95	96.27 106	98.92 55	99.50 30	97.63 57	98.85 88	98.90 42	84.80 325	97.77 81	99.11 49	92.84 84	99.66 100	94.85 138	99.77 20	99.47 80

LS3D		97.16 87	96.66 94	98.68 65	98.53 135	97.19 74	98.93 72	98.90 42	92.83 209	95.99 163	99.37 13	92.12 98	99.87 38	93.67 169	99.57 58	98.97 131

DELS-MVS		98.40 33	98.20 36	98.99 49	99.00 89	97.66 55	97.75 246	98.89 44	97.71 6	98.33 56	98.97 68	94.97 55	99.88 37	98.42 16	99.76 26	99.42 88
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
DP-MVS Recon		97.86 53	97.46 60	99.06 47	99.53 27	98.35 25	98.33 179	98.89 44	92.62 212	98.05 63	98.94 75	95.34 45	99.65 101	96.04 103	99.42 78	99.19 109

AdaColmap		97.15 88	96.70 90	98.48 79	99.16 79	96.69 91	98.01 218	98.89 44	94.44 138	96.83 121	98.68 97	90.69 125	99.76 84	94.36 150	99.29 86	98.98 130

XVS		98.70 5	98.49 12	99.34 15	99.70 15	98.35 25	99.29 14	98.88 47	97.40 14	98.46 48	99.20 38	95.90 32	99.89 29	97.85 35	99.74 35	99.78 7

X-MVStestdata		94.06 244	92.30 263	99.34 15	99.70 15	98.35 25	99.29 14	98.88 47	97.40 14	98.46 48	43.50 353	95.90 32	99.89 29	97.85 35	99.74 35	99.78 7

CP-MVS		98.57 22	98.36 19	99.19 30	99.66 19	97.86 49	99.34 11	98.87 49	95.96 70	98.60 44	99.13 47	96.05 25	99.94 3	97.77 40	99.86 1	99.77 14

SteuartSystems-ACMMP		98.90 2	98.75 2	99.36 14	99.22 74	98.43 19	99.10 51	98.87 49	97.38 17	99.35 6	99.40 7	97.78 1	99.87 38	97.77 40	99.85 2	99.78 7
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	96.37 2	97.93 50	98.48 13	96.30 232	99.00 89	89.54 295	97.43 265	98.87 49	98.16 2	99.26 9	99.38 12	96.12 20	99.64 103	98.30 21	99.77 20	99.72 33

DTE-MVSNet		93.98 246	93.26 249	96.14 238	96.06 284	94.39 218	99.20 32	98.86 52	93.06 198	91.78 275	97.81 173	85.87 238	97.58 299	90.53 244	86.17 312	96.46 283

SD-MVS		98.64 11	98.68 3	98.53 75	99.33 45	98.36 24	98.90 74	98.85 53	97.28 21	99.72 1	99.39 8	96.63 10	97.60 298	98.17 23	99.85 2	99.64 56

test_part1								98.84 54				97.38 2			99.78 15	99.76 20

ESAPD		98.70 5	98.39 15	99.62 1	99.63 21	99.18 1	98.55 153	98.84 54	96.40 57	99.27 7	99.31 22	97.38 2	99.93 9	96.37 96	99.78 15	99.76 20

test_prior3		98.22 44	97.90 45	99.19 30	99.31 50	98.22 33	97.80 242	98.84 54	96.12 65	97.89 78	98.69 95	95.96 28	99.70 94	96.89 72	99.60 52	99.65 53

test_prior				99.19 30	99.31 50	98.22 33		98.84 54					99.70 94			99.65 53

SMA-MVS		98.64 11	98.33 25	99.59 2	99.51 28	99.11 3	98.95 69	98.83 58	93.77 161	99.52 3	99.52 3	96.94 5	99.89 29	98.06 25	99.84 7	99.76 20

abl_6		98.30 42	98.03 40	99.13 40	99.56 26	97.76 54	99.13 47	98.82 59	96.14 63	99.26 9	99.37 13	93.33 79	99.93 9	96.96 68	99.67 42	99.69 38

HPM-MVS_fast		98.38 34	98.13 37	99.12 42	99.75 3	97.86 49	99.44 4	98.82 59	94.46 137	98.94 24	99.20 38	95.16 51	99.74 88	97.58 48	99.85 2	99.77 14

APD-MVS		98.35 37	98.00 42	99.42 11	99.51 28	98.72 10	98.80 103	98.82 59	94.52 133	99.23 11	99.25 31	95.54 40	99.80 60	96.52 90	99.77 20	99.74 28
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_Plus		98.61 15	98.30 27	99.55 3	99.62 23	98.95 6	98.82 94	98.81 62	95.80 74	99.16 15	99.47 5	95.37 43	99.92 15	97.89 33	99.75 32	99.79 4

Regformer-2		98.69 8	98.52 8	99.19 30	99.35 40	98.01 44	98.37 175	98.81 62	97.48 11	99.21 12	99.21 35	96.13 19	99.80 60	98.40 18	99.73 37	99.75 23

APD-MVS_3200maxsize		98.53 27	98.33 25	99.15 39	99.50 30	97.92 48	99.15 43	98.81 62	96.24 60	99.20 13	99.37 13	95.30 46	99.80 60	97.73 42	99.67 42	99.72 33

WR-MVS		95.15 180	94.46 185	97.22 158	96.67 242	96.45 100	98.21 193	98.81 62	94.15 141	93.16 244	97.69 181	87.51 205	98.30 267	95.29 130	88.62 287	96.90 226

mPP-MVS		98.51 28	98.26 30	99.25 26	99.75 3	98.04 42	99.28 16	98.81 62	96.24 60	98.35 55	99.23 32	95.46 41	99.94 3	97.42 56	99.81 9	99.77 14

CNVR-MVS		98.78 3	98.56 6	99.45 10	99.32 48	98.87 8	98.47 166	98.81 62	97.72 4	98.76 36	99.16 45	97.05 4	99.78 77	98.06 25	99.66 45	99.69 38

CPTT-MVS		97.72 58	97.32 65	98.92 55	99.64 20	97.10 76	99.12 49	98.81 62	92.34 229	98.09 61	99.08 57	93.01 83	99.92 15	96.06 102	99.77 20	99.75 23

HPM-MVS		98.36 36	98.10 38	99.13 40	99.74 7	97.82 52	99.53 1	98.80 69	94.63 130	98.61 43	98.97 68	95.13 52	99.77 82	97.65 45	99.83 8	99.79 4
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
Regformer-4		98.64 11	98.53 7	98.99 49	99.43 38	97.37 66	98.40 173	98.79 70	97.46 12	99.09 16	99.31 22	95.86 34	99.80 60	98.64 4	99.76 26	99.79 4

MP-MVS		98.33 40	98.01 41	99.28 22	99.75 3	98.18 36	99.22 28	98.79 70	96.13 64	97.92 76	99.23 32	94.54 62	99.94 3	96.74 82	99.78 15	99.73 30

CANet		98.05 45	97.76 47	98.90 57	98.73 118	97.27 69	98.35 177	98.78 72	97.37 19	97.72 86	98.96 72	91.53 113	99.92 15	98.79 3	99.65 46	99.51 72

MP-MVS-pluss		98.31 41	97.92 44	99.49 6	99.72 11	98.88 7	98.43 170	98.78 72	94.10 143	97.69 88	99.42 6	95.25 48	99.92 15	98.09 24	99.80 10	99.67 49
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DeepC-MVS_fast	96.70 1	98.55 24	98.34 22	99.18 34	99.25 67	98.04 42	98.50 163	98.78 72	97.72 4	98.92 29	99.28 28	95.27 47	99.82 51	97.55 51	99.77 20	99.69 38
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MG-MVS		97.81 55	97.60 51	98.44 82	99.12 83	95.97 118	97.75 246	98.78 72	96.89 42	98.46 48	99.22 34	93.90 76	99.68 98	94.81 140	99.52 69	99.67 49

Regformer-1		98.66 9	98.51 10	99.12 42	99.35 40	97.81 53	98.37 175	98.76 76	97.49 10	99.20 13	99.21 35	96.08 22	99.79 72	98.42 16	99.73 37	99.75 23

NCCC		98.61 15	98.35 21	99.38 12	99.28 63	98.61 13	98.45 167	98.76 76	97.82 3	98.45 51	98.93 76	96.65 9	99.83 46	97.38 58	99.41 79	99.71 35

PLC	95.07 4	97.20 85	96.78 87	98.44 82	99.29 58	96.31 108	98.14 204	98.76 76	92.41 226	96.39 154	98.31 133	94.92 56	99.78 77	94.06 159	98.77 104	99.23 105
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DeepC-MVS	95.98 3	97.88 51	97.58 52	98.77 61	99.25 67	96.93 81	98.83 92	98.75 79	96.96 41	96.89 118	99.50 4	90.46 127	99.87 38	97.84 37	99.76 26	99.52 69
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_0304		97.70 59	97.25 67	99.07 45	98.90 99	97.83 51	98.20 194	98.74 80	97.51 8	98.03 66	99.06 59	86.12 227	99.93 9	99.02 1	99.64 48	99.44 87

zzz-MVS		98.55 24	98.25 31	99.46 8	99.76 1	98.64 11	98.55 153	98.74 80	97.27 25	98.02 67	99.39 8	94.81 57	99.96 1	97.91 30	99.79 11	99.77 14

MTGPA								98.74 80

MTAPA		98.58 20	98.29 28	99.46 8	99.76 1	98.64 11	98.90 74	98.74 80	97.27 25	98.02 67	99.39 8	94.81 57	99.96 1	97.91 30	99.79 11	99.77 14

ab-mvs		96.42 113	95.71 125	98.55 73	98.63 128	96.75 89	97.88 235	98.74 80	93.84 157	96.54 138	98.18 143	85.34 248	99.75 86	95.93 106	96.35 173	99.15 115

TEST9					99.31 50	98.50 15	97.92 226	98.73 85	92.63 211	97.74 84	98.68 97	96.20 15	99.80 60

train_agg		97.97 46	97.52 56	99.33 17	99.31 50	98.50 15	97.92 226	98.73 85	92.98 202	97.74 84	98.68 97	96.20 15	99.80 60	96.59 86	99.57 58	99.68 44

test_8					99.29 58	98.44 17	97.89 234	98.72 87	92.98 202	97.70 87	98.66 100	96.20 15	99.80 60

agg_prior3		97.87 52	97.42 62	99.23 29	99.29 58	98.23 31	97.92 226	98.72 87	92.38 228	97.59 95	98.64 102	96.09 21	99.79 72	96.59 86	99.57 58	99.68 44

agg_prior1		97.95 48	97.51 57	99.28 22	99.30 55	98.38 20	97.81 241	98.72 87	93.16 196	97.57 96	98.66 100	96.14 18	99.81 53	96.63 85	99.56 64	99.66 51

agg_prior					99.30 55	98.38 20		98.72 87		97.57 96			99.81 53

无先验							97.58 258	98.72 87	91.38 253				99.87 38	93.36 175		99.60 62

WTY-MVS		97.37 79	96.92 81	98.72 63	98.86 110	96.89 85	98.31 184	98.71 92	95.26 103	97.67 89	98.56 109	92.21 95	99.78 77	95.89 107	96.85 156	99.48 79

3Dnovator+	94.38 6	97.43 74	96.78 87	99.38 12	97.83 172	98.52 14	99.37 7	98.71 92	97.09 37	92.99 251	99.13 47	89.36 139	99.89 29	96.97 66	99.57 58	99.71 35

旧先验1					99.29 58	97.48 62		98.70 94			99.09 55	95.56 38			99.47 72	99.61 59

EI-MVSNet-Vis-set		98.47 30	98.39 15	98.69 64	99.46 35	96.49 99	98.30 186	98.69 95	97.21 28	98.84 30	99.36 17	95.41 42	99.78 77	98.62 6	99.65 46	99.80 3

新几何1				99.16 37	99.34 42	98.01 44		98.69 95	90.06 280	98.13 59	98.95 74	94.60 61	99.89 29	91.97 216	99.47 72	99.59 64

API-MVS		97.41 76	97.25 67	97.91 111	98.70 121	96.80 86	98.82 94	98.69 95	94.53 132	98.11 60	98.28 134	94.50 66	99.57 117	94.12 158	99.49 70	97.37 200

EI-MVSNet-UG-set		98.41 32	98.34 22	98.61 69	99.45 36	96.32 106	98.28 188	98.68 98	97.17 31	98.74 37	99.37 13	95.25 48	99.79 72	98.57 8	99.54 67	99.73 30

Regformer-3		98.59 18	98.50 11	98.86 59	99.43 38	97.05 77	98.40 173	98.68 98	97.43 13	99.06 17	99.31 22	95.80 35	99.77 82	98.62 6	99.76 26	99.78 7

testdata				98.26 91	99.20 77	95.36 154		98.68 98	91.89 239	98.60 44	99.10 51	94.44 68	99.82 51	94.27 154	99.44 77	99.58 66

1121		97.37 79	96.77 89	99.16 37	99.34 42	97.99 47	98.19 198	98.68 98	90.14 278	98.01 69	98.97 68	94.80 59	99.87 38	93.36 175	99.46 75	99.61 59

MCST-MVS		98.65 10	98.37 18	99.48 7	99.60 24	98.87 8	98.41 172	98.68 98	97.04 38	98.52 47	98.80 87	96.78 7	99.83 46	97.93 29	99.61 51	99.74 28

PVSNet	91.96 18	96.35 115	96.15 110	96.96 174	99.17 78	92.05 262	96.08 314	98.68 98	93.69 170	97.75 83	97.80 174	88.86 156	99.69 97	94.26 155	99.01 92	99.15 115

MAR-MVS		96.91 96	96.40 102	98.45 81	98.69 123	96.90 83	98.66 138	98.68 98	92.40 227	97.07 107	97.96 157	91.54 112	99.75 86	93.68 168	98.92 95	98.69 147
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
原ACMM1				98.65 67	99.32 48	96.62 92		98.67 105	93.27 194	97.81 80	98.97 68	95.18 50	99.83 46	93.84 164	99.46 75	99.50 74

CDPH-MVS		97.94 49	97.49 58	99.28 22	99.47 34	98.44 17	97.91 229	98.67 105	92.57 215	98.77 35	98.85 82	95.93 30	99.72 89	95.56 121	99.69 41	99.68 44

UnsupCasMVSNet_eth		90.99 291	89.92 292	94.19 300	94.08 322	89.83 291	97.13 285	98.67 105	93.69 170	85.83 313	96.19 286	75.15 322	96.74 319	89.14 275	79.41 331	96.00 296

TSAR-MVS + MP.		98.78 3	98.62 4	99.24 27	99.69 17	98.28 30	99.14 44	98.66 108	96.84 43	99.56 2	99.31 22	96.34 13	99.70 94	98.32 20	99.73 37	99.73 30

HPM-MVS++		98.58 20	98.25 31	99.55 3	99.50 30	99.08 4	98.72 124	98.66 108	97.51 8	98.15 58	98.83 84	95.70 36	99.92 15	97.53 53	99.67 42	99.66 51

test222					99.23 73	97.17 75	97.40 266	98.66 108	88.68 304	98.05 63	98.96 72	94.14 72			99.53 68	99.61 59

test11								98.66 108

XXY-MVS		95.20 179	94.45 187	97.46 148	96.75 237	96.56 96	98.86 87	98.65 112	93.30 193	93.27 241	98.27 137	84.85 255	98.87 198	94.82 139	91.26 257	96.96 216

TAPA-MVS	93.98 7	95.35 170	94.56 181	97.74 120	99.13 82	94.83 191	98.33 179	98.64 113	86.62 313	96.29 156	98.61 103	94.00 75	99.29 143	80.00 325	99.41 79	99.09 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
F-COLMAP		97.09 91	96.80 84	97.97 109	99.45 36	94.95 173	98.55 153	98.62 114	93.02 200	96.17 158	98.58 108	94.01 74	99.81 53	93.95 161	98.90 96	99.14 117

PAPM_NR		97.46 69	97.11 73	98.50 77	99.50 30	96.41 102	98.63 140	98.60 115	95.18 107	97.06 108	98.06 150	94.26 71	99.57 117	93.80 166	98.87 99	99.52 69

cdsmvs_eth3d_5k		23.98 332	31.98 332	0.00 347	0.00 361	0.00 362	0.00 353	98.59 116	0.00 357	0.00 358	98.61 103	90.60 126	0.00 360	0.00 357	0.00 358	0.00 358

1314		96.25 122	95.73 121	97.79 118	97.13 217	95.55 149	98.19 198	98.59 116	93.47 180	92.03 274	97.82 172	91.33 115	99.49 129	94.62 143	98.44 118	98.32 171

CVMVSNet		95.43 160	96.04 113	93.57 304	97.93 166	83.62 327	98.12 207	98.59 116	95.68 77	96.56 134	99.02 61	87.51 205	97.51 301	93.56 172	97.44 148	99.60 62

OMC-MVS		97.55 68	97.34 64	98.20 94	99.33 45	95.92 132	98.28 188	98.59 116	95.52 85	97.97 72	99.10 51	93.28 81	99.49 129	95.09 135	98.88 97	99.19 109

LTVRE_ROB	92.95 15	94.60 213	93.90 217	96.68 192	97.41 200	94.42 216	98.52 158	98.59 116	91.69 244	91.21 279	98.35 126	84.87 254	99.04 177	91.06 235	93.44 231	96.60 267
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
HSP-MVS		98.70 5	98.52 8	99.24 27	99.75 3	98.23 31	99.26 17	98.58 121	97.52 7	99.41 4	98.78 88	96.00 26	99.79 72	97.79 39	99.59 55	99.69 38

PAPR		96.84 99	96.24 108	98.65 67	98.72 120	96.92 82	97.36 272	98.57 122	93.33 190	96.67 128	97.57 192	94.30 70	99.56 119	91.05 237	98.59 111	99.47 80

HQP_MVS		96.14 123	95.90 117	96.85 180	97.42 197	94.60 211	98.80 103	98.56 123	97.28 21	95.34 167	98.28 134	87.09 211	99.03 178	96.07 100	94.27 207	96.92 219

plane_prior5								98.56 123					99.03 178	96.07 100	94.27 207	96.92 219

mvs_tets		95.41 164	95.00 151	96.65 197	95.58 302	94.42 216	99.00 62	98.55 125	95.73 76	93.21 243	98.38 123	83.45 282	98.63 215	97.09 64	94.00 218	96.91 224

pcd1.5k->3k		39.42 330	41.78 331	32.35 343	96.17 278	0.00 362	0.00 353	98.54 126	0.00 357	0.00 358	0.00 359	87.78 198	0.00 360	0.00 357	93.56 227	97.06 210

LPG-MVS_test		95.62 143	95.34 136	96.47 219	97.46 193	93.54 240	98.99 63	98.54 126	94.67 126	94.36 198	98.77 90	85.39 245	99.11 166	95.71 116	94.15 213	96.76 240

LGP-MVS_train				96.47 219	97.46 193	93.54 240		98.54 126	94.67 126	94.36 198	98.77 90	85.39 245	99.11 166	95.71 116	94.15 213	96.76 240

test12				99.18 34	99.16 79	98.19 35		98.53 129		98.07 62		95.13 52	99.72 89		99.56 64	99.63 58

CNLPA		97.45 72	97.03 77	98.73 62	99.05 84	97.44 65	98.07 213	98.53 129	95.32 101	96.80 125	98.53 110	93.32 80	99.72 89	94.31 153	99.31 85	99.02 126

jajsoiax		95.45 159	95.03 150	96.73 185	95.42 308	94.63 206	99.14 44	98.52 131	95.74 75	93.22 242	98.36 125	83.87 279	98.65 214	96.95 69	94.04 216	96.91 224

XVG-OURS		96.55 109	96.41 101	96.99 171	98.75 117	93.76 235	97.50 262	98.52 131	95.67 78	96.83 121	99.30 27	88.95 153	99.53 126	95.88 108	96.26 182	97.69 191

xiu_mvs_v1_base_debu		97.60 63	97.56 53	97.72 121	98.35 137	95.98 114	97.86 237	98.51 133	97.13 34	99.01 20	98.40 120	91.56 109	99.80 60	98.53 10	98.68 105	97.37 200

xiu_mvs_v1_base		97.60 63	97.56 53	97.72 121	98.35 137	95.98 114	97.86 237	98.51 133	97.13 34	99.01 20	98.40 120	91.56 109	99.80 60	98.53 10	98.68 105	97.37 200

xiu_mvs_v1_base_debi		97.60 63	97.56 53	97.72 121	98.35 137	95.98 114	97.86 237	98.51 133	97.13 34	99.01 20	98.40 120	91.56 109	99.80 60	98.53 10	98.68 105	97.37 200

PS-MVSNAJ		97.73 57	97.77 46	97.62 132	98.68 124	95.58 145	97.34 274	98.51 133	97.29 20	98.66 40	97.88 164	94.51 63	99.90 27	97.87 34	99.17 89	97.39 198

cascas		94.63 212	93.86 219	96.93 177	96.91 228	94.27 223	96.00 318	98.51 133	85.55 321	94.54 184	96.23 283	84.20 273	98.87 198	95.80 112	96.98 155	97.66 192

PS-MVSNAJss		96.43 112	96.26 107	96.92 179	95.84 294	95.08 165	99.16 42	98.50 138	95.87 72	93.84 228	98.34 130	94.51 63	98.61 216	96.88 75	93.45 230	97.06 210

MVS		94.67 210	93.54 239	98.08 103	96.88 230	96.56 96	98.19 198	98.50 138	78.05 339	92.69 256	98.02 152	91.07 120	99.63 106	90.09 255	98.36 122	98.04 176

XVG-OURS-SEG-HR		96.51 110	96.34 103	97.02 170	98.77 116	93.76 235	97.79 244	98.50 138	95.45 87	96.94 113	99.09 55	87.87 195	99.55 125	96.76 81	95.83 199	97.74 187

PVSNet_0	88.72 19	91.28 287	90.03 290	95.00 279	97.99 163	87.29 321	94.84 331	98.50 138	92.06 236	89.86 291	95.19 302	79.81 302	99.39 137	92.27 207	69.79 344	98.33 170

ACMH	92.88 16	94.55 217	93.95 214	96.34 230	97.63 180	93.26 247	98.81 100	98.49 142	93.43 181	89.74 292	98.53 110	81.91 288	99.08 171	93.69 167	93.30 234	96.70 249
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
xiu_mvs_v2_base		97.66 62	97.70 49	97.56 140	98.61 130	95.46 151	97.44 263	98.46 143	97.15 32	98.65 41	98.15 144	94.33 69	99.80 60	97.84 37	98.66 109	97.41 196

HQP3-MVS								98.46 143							94.18 211

HQP-MVS		95.72 136	95.40 131	96.69 189	97.20 211	94.25 224	98.05 214	98.46 143	96.43 54	94.45 188	97.73 177	86.75 217	98.96 185	95.30 128	94.18 211	96.86 232

CLD-MVS		95.62 143	95.34 136	96.46 222	97.52 190	93.75 237	97.27 279	98.46 143	95.53 84	94.42 196	98.00 155	86.21 225	98.97 182	96.25 99	94.37 205	96.66 258
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
XVG-ACMP-BASELINE		94.54 218	94.14 201	95.75 253	96.55 245	91.65 270	98.11 209	98.44 147	94.96 118	94.22 210	97.90 162	79.18 306	99.11 166	94.05 160	93.85 221	96.48 282

ACMP	93.49 10	95.34 171	94.98 153	96.43 223	97.67 178	93.48 242	98.73 122	98.44 147	94.94 121	92.53 261	98.53 110	84.50 264	99.14 160	95.48 124	94.00 218	96.66 258
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	93.85 9	95.69 140	95.38 135	96.61 203	97.61 182	93.84 233	98.91 73	98.44 147	95.25 104	94.28 206	98.47 116	86.04 237	99.12 162	95.50 123	93.95 220	96.87 230
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+		97.12 89	96.69 91	98.39 86	98.19 150	96.72 90	97.37 270	98.43 150	93.71 167	97.65 92	98.02 152	92.20 96	99.25 145	96.87 78	97.79 141	99.19 109

anonymousdsp		95.42 162	94.91 161	96.94 176	95.10 312	95.90 135	99.14 44	98.41 151	93.75 162	93.16 244	97.46 196	87.50 207	98.41 254	95.63 120	94.03 217	96.50 280

PMMVS		96.60 105	96.33 104	97.41 151	97.90 168	93.93 230	97.35 273	98.41 151	92.84 208	97.76 82	97.45 198	91.10 119	99.20 155	96.26 98	97.91 135	99.11 119

MVSFormer		97.57 66	97.49 58	97.84 114	98.07 157	95.76 140	99.47 2	98.40 153	94.98 116	98.79 33	98.83 84	92.34 89	98.41 254	96.91 70	99.59 55	99.34 91

test_djsdf		96.00 125	95.69 127	96.93 177	95.72 298	95.49 150	99.47 2	98.40 153	94.98 116	94.58 183	97.86 165	89.16 145	98.41 254	96.91 70	94.12 215	96.88 229

OPM-MVS		95.69 140	95.33 138	96.76 184	96.16 281	94.63 206	98.43 170	98.39 155	96.64 50	95.02 173	98.78 88	85.15 250	99.05 173	95.21 134	94.20 210	96.60 267

canonicalmvs		97.67 61	97.23 69	98.98 51	98.70 121	98.38 20	99.34 11	98.39 155	96.76 45	97.67 89	97.40 200	92.26 92	99.49 129	98.28 22	96.28 181	99.08 123

DP-MVS		96.59 107	95.93 116	98.57 71	99.34 42	96.19 110	98.70 128	98.39 155	89.45 297	94.52 185	99.35 19	91.85 104	99.85 43	92.89 194	98.88 97	99.68 44

ACMH+	92.99 14	94.30 228	93.77 225	95.88 247	97.81 173	92.04 263	98.71 125	98.37 158	93.99 149	90.60 287	98.47 116	80.86 295	99.05 173	92.75 196	92.40 243	96.55 274

MSDG		95.93 128	95.30 141	97.83 115	98.90 99	95.36 154	96.83 301	98.37 158	91.32 258	94.43 195	98.73 94	90.27 131	99.60 109	90.05 258	98.82 102	98.52 156

CMPMVS	66.06 21	89.70 299	89.67 294	89.78 318	93.19 325	76.56 338	97.00 287	98.35 160	80.97 335	81.57 332	97.75 176	74.75 325	98.61 216	89.85 261	93.63 225	94.17 329
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v7n		94.19 234	93.43 245	96.47 219	95.90 290	94.38 219	99.26 17	98.34 161	91.99 237	92.76 255	97.13 224	88.31 180	98.52 230	89.48 271	87.70 297	96.52 277

CDS-MVSNet		96.99 93	96.69 91	97.90 112	98.05 160	95.98 114	98.20 194	98.33 162	93.67 174	96.95 111	98.49 114	93.54 77	98.42 247	95.24 133	97.74 144	99.31 94
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
nrg030		96.28 120	95.72 122	97.96 110	96.90 229	98.15 38	99.39 5	98.31 163	95.47 86	94.42 196	98.35 126	92.09 99	98.69 210	97.50 54	89.05 276	97.04 212

TAMVS		97.02 92	96.79 86	97.70 126	98.06 159	95.31 158	98.52 158	98.31 163	93.95 152	97.05 109	98.61 103	93.49 78	98.52 230	95.33 127	97.81 140	99.29 99

EPP-MVSNet		97.46 69	97.28 66	97.99 108	98.64 127	95.38 153	99.33 13	98.31 163	93.61 176	97.19 102	99.07 58	94.05 73	99.23 147	96.89 72	98.43 120	99.37 90

UnsupCasMVSNet_bld		87.17 308	85.12 311	93.31 307	91.94 329	88.77 305	94.92 330	98.30 166	84.30 327	82.30 329	90.04 337	63.96 343	97.25 305	85.85 310	74.47 343	93.93 334

Vis-MVSNet (Re-imp)		96.87 98	96.55 97	97.83 115	98.73 118	95.46 151	99.20 32	98.30 166	94.96 118	96.60 133	98.87 81	90.05 134	98.59 219	93.67 169	98.60 110	99.46 84

TSAR-MVS + GP.		98.38 34	98.24 33	98.81 60	99.22 74	97.25 72	98.11 209	98.29 168	97.19 30	98.99 23	99.02 61	96.22 14	99.67 99	98.52 14	98.56 113	99.51 72

MS-PatchMatch		93.84 249	93.63 233	94.46 296	96.18 277	89.45 296	97.76 245	98.27 169	92.23 234	92.13 272	97.49 194	79.50 303	98.69 210	89.75 264	99.38 82	95.25 309

EI-MVSNet		95.96 126	95.83 119	96.36 227	97.93 166	93.70 239	98.12 207	98.27 169	93.70 169	95.07 171	99.02 61	92.23 94	98.54 223	94.68 141	93.46 228	96.84 233

MVSTER		96.06 124	95.72 122	97.08 168	98.23 146	95.93 125	98.73 122	98.27 169	94.86 122	95.07 171	98.09 148	88.21 182	98.54 223	96.59 86	93.46 228	96.79 237

FMVSNet2		94.47 221	93.61 235	97.04 169	98.21 147	96.43 101	98.79 108	98.27 169	92.46 216	93.50 237	97.09 227	81.16 290	98.00 283	91.09 233	91.93 248	96.70 249

FMVSNet3		94.97 188	94.26 193	97.11 166	98.18 152	96.62 92	98.56 151	98.26 173	93.67 174	94.09 217	97.10 225	84.25 270	98.01 282	92.08 210	92.14 244	96.70 249

Fast-Effi-MVS+		96.28 120	95.70 126	98.03 107	98.29 143	95.97 118	98.58 146	98.25 174	91.74 243	95.29 170	97.23 213	91.03 121	99.15 158	92.90 192	97.96 134	98.97 131

PAPM		94.95 189	94.00 210	97.78 119	97.04 220	95.65 143	96.03 317	98.25 174	91.23 263	94.19 212	97.80 174	91.27 116	98.86 200	82.61 320	97.61 146	98.84 140

v748		93.75 250	93.06 250	95.82 249	95.73 297	92.64 256	99.25 19	98.24 176	91.60 246	92.22 270	96.52 274	87.60 204	98.46 239	90.64 242	85.72 315	96.36 286

diffmvs		96.32 117	95.74 120	98.07 105	98.26 144	96.14 111	98.53 157	98.23 177	90.10 279	96.88 119	97.73 177	90.16 133	99.15 158	93.90 163	97.85 139	98.91 137

V4		94.18 236	93.52 240	96.13 239	95.89 291	94.31 221	99.23 22	98.22 178	91.42 251	92.82 254	96.89 255	87.93 192	98.52 230	91.51 228	87.81 294	95.58 306

CANet_DTU		96.96 94	96.55 97	98.21 93	98.17 154	96.07 113	97.98 221	98.21 179	97.24 27	97.13 103	98.93 76	86.88 216	99.91 24	95.00 136	99.37 83	98.66 150

v52		94.18 236	93.52 240	96.13 239	95.95 289	94.29 222	99.23 22	98.21 179	91.42 251	92.84 253	96.89 255	87.85 196	98.53 229	91.51 228	87.81 294	95.57 307

HY-MVS	93.96 8	96.82 100	96.23 109	98.57 71	98.46 136	97.00 78	98.14 204	98.21 179	93.95 152	96.72 127	97.99 156	91.58 108	99.76 84	94.51 148	96.54 164	98.95 135

PCF-MVS	93.45 11	94.68 209	93.43 245	98.42 85	98.62 129	96.77 88	95.48 324	98.20 182	84.63 326	93.34 240	98.32 132	88.55 175	99.81 53	84.80 316	98.96 94	98.68 148
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v8		94.47 221	93.77 225	96.57 209	96.36 258	94.83 191	99.05 57	98.19 183	91.92 238	93.16 244	96.97 244	88.82 161	98.48 234	91.69 224	87.79 296	96.39 284

v10		94.29 229	93.55 238	96.51 216	96.39 254	94.80 196	98.99 63	98.19 183	91.35 256	93.02 250	96.99 242	88.09 187	98.41 254	90.50 251	88.41 289	96.33 288

mvs_anonymous		96.70 103	96.53 99	97.18 161	98.19 150	93.78 234	98.31 184	98.19 183	94.01 147	94.47 187	98.27 137	92.08 100	98.46 239	97.39 57	97.91 135	99.31 94

AllTest		95.24 176	94.65 177	96.99 171	99.25 67	93.21 249	98.59 144	98.18 186	91.36 254	93.52 235	98.77 90	84.67 256	99.72 89	89.70 266	97.87 137	98.02 177

TestCases				96.99 171	99.25 67	93.21 249		98.18 186	91.36 254	93.52 235	98.77 90	84.67 256	99.72 89	89.70 266	97.87 137	98.02 177

GBi-Net		94.49 219	93.80 222	96.56 210	98.21 147	95.00 167	98.82 94	98.18 186	92.46 216	94.09 217	97.07 229	81.16 290	97.95 285	92.08 210	92.14 244	96.72 245

test1		94.49 219	93.80 222	96.56 210	98.21 147	95.00 167	98.82 94	98.18 186	92.46 216	94.09 217	97.07 229	81.16 290	97.95 285	92.08 210	92.14 244	96.72 245

FMVSNet1		93.19 262	92.07 265	96.56 210	97.54 188	95.00 167	98.82 94	98.18 186	90.38 274	92.27 268	97.07 229	73.68 329	97.95 285	89.36 273	91.30 255	96.72 245

v1192		94.32 227	93.58 237	96.53 214	96.10 282	94.45 215	98.50 163	98.17 191	91.54 247	94.19 212	97.06 232	86.95 215	98.43 246	90.14 254	89.57 268	96.70 249

v1240		94.06 244	93.29 248	96.34 230	96.03 286	93.90 231	98.44 168	98.17 191	91.18 265	94.13 216	97.01 241	86.05 235	98.42 247	89.13 276	89.50 271	96.70 249

v144192		94.39 225	93.70 230	96.48 218	96.06 284	94.35 220	98.58 146	98.16 193	91.45 249	94.33 200	97.02 239	87.50 207	98.45 241	91.08 234	89.11 275	96.63 263

Fast-Effi-MVS+-dtu		95.87 130	95.85 118	95.91 245	97.74 176	91.74 269	98.69 129	98.15 194	95.56 83	94.92 174	97.68 184	88.98 151	98.79 207	93.19 180	97.78 142	97.20 208

v1921920		94.20 233	93.47 244	96.40 225	95.98 287	94.08 227	98.52 158	98.15 194	91.33 257	94.25 208	97.20 215	86.41 222	98.42 247	90.04 259	89.39 273	96.69 254

v1144		94.59 215	93.92 215	96.60 204	96.21 275	94.78 201	98.59 144	98.14 196	91.86 242	94.21 211	97.02 239	87.97 190	98.41 254	91.72 223	89.57 268	96.61 265

v7		94.69 206	94.04 207	96.62 202	96.41 253	94.79 199	98.78 110	98.13 197	91.89 239	94.30 204	97.16 216	88.13 186	98.45 241	91.96 217	89.65 267	96.61 265

IterMVS-LS		95.46 158	95.21 144	96.22 235	98.12 155	93.72 238	98.32 183	98.13 197	93.71 167	94.26 207	97.31 208	92.24 93	98.10 276	94.63 142	90.12 262	96.84 233
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1141		94.75 202	94.11 205	96.67 195	96.27 273	94.86 179	98.69 129	98.12 199	92.43 224	94.31 202	96.94 248	88.78 166	98.48 234	92.63 199	88.85 284	96.67 255

divwei89l23v2f112		94.76 200	94.12 204	96.67 195	96.28 271	94.85 180	98.69 129	98.12 199	92.44 223	94.29 205	96.94 248	88.85 158	98.48 234	92.67 197	88.79 286	96.67 255

v1		94.75 202	94.11 205	96.69 189	96.27 273	94.87 178	98.69 129	98.12 199	92.43 224	94.32 201	96.94 248	88.71 170	98.54 223	92.66 198	88.84 285	96.67 255

EU-MVSNet		93.66 251	94.14 201	92.25 313	95.96 288	83.38 328	98.52 158	98.12 199	94.69 124	92.61 258	98.13 146	87.36 209	96.39 327	91.82 219	90.00 264	96.98 215

IterMVS		94.09 241	93.85 220	94.80 287	97.99 163	90.35 288	97.18 283	98.12 199	93.68 172	92.46 265	97.34 205	84.05 275	97.41 303	92.51 204	91.33 254	96.62 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
semantic-postprocess				94.85 284	97.98 165	90.56 286		98.11 204	93.75 162	92.58 259	97.48 195	83.91 277	97.41 303	92.48 205	91.30 255	96.58 269

v1neww		94.83 194	94.22 194	96.68 192	96.39 254	94.85 180	98.87 81	98.11 204	92.45 221	94.45 188	97.06 232	88.82 161	98.54 223	92.93 189	88.91 280	96.65 260

v7new		94.83 194	94.22 194	96.68 192	96.39 254	94.85 180	98.87 81	98.11 204	92.45 221	94.45 188	97.06 232	88.82 161	98.54 223	92.93 189	88.91 280	96.65 260

v6		94.83 194	94.21 196	96.69 189	96.36 258	94.85 180	98.87 81	98.11 204	92.46 216	94.44 194	97.05 236	88.76 167	98.57 221	92.95 188	88.92 279	96.65 260

COLMAP_ROB	93.27 12	95.33 172	94.87 163	96.71 186	99.29 58	93.24 248	98.58 146	98.11 204	89.92 285	93.57 233	99.10 51	86.37 223	99.79 72	90.78 239	98.10 131	97.09 209
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Effi-MVS+-dtu		96.29 118	96.56 96	95.51 257	97.89 169	90.22 289	98.80 103	98.10 209	96.57 52	96.45 153	96.66 267	90.81 122	98.91 192	95.72 114	97.99 133	97.40 197

mvs-test1		96.60 105	96.68 93	96.37 226	97.89 169	91.81 265	98.56 151	98.10 209	96.57 52	96.52 140	97.94 159	90.81 122	99.45 134	95.72 114	98.01 132	97.86 184

1112_ss		96.63 104	96.00 115	98.50 77	98.56 132	96.37 103	98.18 202	98.10 209	92.92 204	94.84 176	98.43 118	92.14 97	99.58 116	94.35 151	96.51 165	99.56 68

V42		94.78 199	94.14 201	96.70 188	96.33 265	95.22 160	98.97 67	98.09 212	92.32 231	94.31 202	97.06 232	88.39 179	98.55 222	92.90 192	88.87 282	96.34 287

v2v482		94.69 206	94.03 208	96.65 197	96.17 278	94.79 199	98.67 136	98.08 213	92.72 210	94.00 222	97.16 216	87.69 202	98.45 241	92.91 191	88.87 282	96.72 245

MVS_Test		97.28 82	97.00 78	98.13 99	98.33 141	95.97 118	98.74 119	98.07 214	94.27 140	98.44 52	98.07 149	92.48 88	99.26 144	96.43 93	98.19 128	99.16 114

Test_1112_low_res		96.34 116	95.66 129	98.36 87	98.56 132	95.94 122	97.71 248	98.07 214	92.10 235	94.79 180	97.29 209	91.75 105	99.56 119	94.17 156	96.50 166	99.58 66

alignmvs		97.56 67	97.07 76	99.01 48	98.66 125	98.37 23	98.83 92	98.06 216	96.74 46	98.00 71	97.65 185	90.80 124	99.48 133	98.37 19	96.56 163	99.19 109

testing_2		90.61 295	88.50 302	96.95 175	90.08 335	95.57 146	97.69 250	98.06 216	93.02 200	76.55 337	92.48 333	61.18 344	98.44 244	95.45 125	91.98 247	96.84 233

RPSCF		94.87 193	95.40 131	93.26 308	98.89 107	82.06 333	98.33 179	98.06 216	90.30 275	96.56 134	99.26 30	87.09 211	99.49 129	93.82 165	96.32 175	98.24 172

Test4		92.21 271	90.34 287	97.82 117	92.83 327	95.87 138	97.94 225	98.05 219	94.50 134	82.12 330	94.48 309	59.54 345	98.54 223	95.39 126	98.22 126	99.06 125

pm-mvs1		93.94 247	93.06 250	96.59 205	96.49 249	95.16 161	98.95 69	98.03 220	92.32 231	91.08 281	97.84 168	84.54 263	98.41 254	92.16 208	86.13 314	96.19 292

v148		94.29 229	93.76 227	95.91 245	96.10 282	92.93 253	98.58 146	97.97 221	92.59 214	93.47 238	96.95 246	88.53 176	98.32 263	92.56 201	87.06 305	96.49 281

IS-MVSNet		97.22 84	96.88 82	98.25 92	98.85 112	96.36 104	99.19 34	97.97 221	95.39 90	97.23 101	98.99 67	91.11 118	98.93 190	94.60 144	98.59 111	99.47 80

pmmvs6		91.77 283	90.63 284	95.17 275	94.69 319	91.24 275	98.67 136	97.92 223	86.14 316	89.62 293	97.56 193	75.79 320	98.34 261	90.75 240	84.56 319	95.94 298

jason		97.32 81	97.08 75	98.06 106	97.45 196	95.59 144	97.87 236	97.91 224	94.79 123	98.55 46	98.83 84	91.12 117	99.23 147	97.58 48	99.60 52	99.34 91
jason: jason.
ppachtmachnet_test		93.22 260	92.63 258	94.97 280	95.45 307	90.84 278	96.88 297	97.88 225	90.60 269	92.08 273	97.26 210	88.08 188	97.86 294	85.12 315	90.33 261	96.22 290

tpm cat1		93.36 254	92.80 254	95.07 278	97.58 185	87.97 316	96.76 302	97.86 226	82.17 333	93.53 234	96.04 290	86.13 226	99.13 161	89.24 274	95.87 198	98.10 175

EG-PatchMatch MVS		91.13 288	90.12 289	94.17 301	94.73 318	89.00 304	98.13 206	97.81 227	89.22 301	85.32 316	96.46 275	67.71 338	98.42 247	87.89 298	93.82 222	95.08 312

BH-untuned		95.95 127	95.72 122	96.65 197	98.55 134	92.26 259	98.23 191	97.79 228	93.73 165	94.62 182	98.01 154	88.97 152	99.00 181	93.04 185	98.51 114	98.68 148

lupinMVS		97.44 73	97.22 70	98.12 100	98.07 157	95.76 140	97.68 251	97.76 229	94.50 134	98.79 33	98.61 103	92.34 89	99.30 141	97.58 48	99.59 55	99.31 94

VDDNet		95.36 169	94.53 182	97.86 113	98.10 156	95.13 163	98.85 88	97.75 230	90.46 271	98.36 54	99.39 8	73.27 330	99.64 103	97.98 28	96.58 162	98.81 141

ADS-MVSNet		95.00 184	94.45 187	96.63 200	98.00 161	91.91 264	96.04 315	97.74 231	90.15 276	96.47 151	96.64 269	87.89 193	98.96 185	90.08 256	97.06 152	99.02 126

tpmvs		94.60 213	94.36 190	95.33 272	97.46 193	88.60 309	96.88 297	97.68 232	91.29 260	93.80 229	96.42 278	88.58 172	99.24 146	91.06 235	96.04 196	98.17 173

pmmvs4		94.69 206	93.99 212	96.81 182	95.74 296	95.94 122	97.40 266	97.67 233	90.42 273	93.37 239	97.59 190	89.08 147	98.20 272	92.97 187	91.67 252	96.30 289

MVP-Stereo		94.28 231	93.92 215	95.35 271	94.95 314	92.60 257	97.97 222	97.65 234	91.61 245	90.68 286	97.09 227	86.32 224	98.42 247	89.70 266	99.34 84	95.02 314
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Patchmatch-test1		95.32 173	94.97 155	96.35 228	97.67 178	91.29 274	97.33 275	97.60 235	94.68 125	96.92 116	96.95 246	83.97 276	98.50 233	91.33 232	98.32 124	99.25 103

GA-MVS		94.81 198	94.03 208	97.14 163	97.15 216	93.86 232	96.76 302	97.58 236	94.00 148	94.76 181	97.04 237	80.91 293	98.48 234	91.79 220	96.25 183	99.09 120

test20.03		90.89 292	90.38 286	92.43 311	93.48 324	88.14 315	98.33 179	97.56 237	93.40 188	87.96 303	96.71 266	80.69 297	94.13 336	79.15 328	86.17 312	95.01 315

CR-MVSNet		94.76 200	94.15 200	96.59 205	97.00 221	93.43 243	94.96 328	97.56 237	92.46 216	96.93 114	96.24 281	88.15 184	97.88 292	87.38 299	96.65 160	98.46 159

Patchmtry		93.22 260	92.35 262	95.84 248	96.77 234	93.09 252	94.66 334	97.56 237	87.37 311	92.90 252	96.24 281	88.15 184	97.90 288	87.37 300	90.10 263	96.53 276

tpmrst		95.63 142	95.69 127	95.44 263	97.54 188	88.54 311	96.97 288	97.56 237	93.50 179	97.52 98	96.93 252	89.49 136	99.16 157	95.25 132	96.42 168	98.64 152

FMVSNet5		91.81 282	90.92 276	94.49 293	97.21 210	92.09 261	98.00 220	97.55 241	89.31 300	90.86 284	95.61 301	74.48 326	95.32 332	85.57 311	89.70 266	96.07 295

testgi		93.06 264	92.45 261	94.88 283	96.43 252	89.90 290	98.75 115	97.54 242	95.60 81	91.63 277	97.91 161	74.46 327	97.02 308	86.10 307	93.67 223	97.72 189

v18		92.10 273	90.97 273	95.50 258	96.34 261	94.85 180	98.82 94	97.52 243	89.99 281	85.31 318	93.26 317	88.90 155	96.92 310	88.82 282	79.77 329	94.73 317

v17		92.08 274	90.94 274	95.48 260	96.34 261	94.83 191	98.81 100	97.52 243	89.95 283	85.32 316	93.24 318	88.91 154	96.91 311	88.76 283	79.63 330	94.71 319

PatchmatchNet		95.71 138	95.52 130	96.29 233	97.58 185	90.72 282	96.84 300	97.52 243	94.06 145	97.08 105	96.96 245	89.24 143	98.90 195	92.03 214	98.37 121	99.26 102
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v16		92.08 274	90.94 274	95.49 259	96.38 257	94.84 189	98.81 100	97.51 246	89.94 284	85.25 319	93.28 316	88.86 156	96.91 311	88.70 284	79.78 328	94.72 318

V14		91.93 277	90.76 279	95.42 268	96.33 265	94.81 195	98.77 111	97.51 246	89.86 287	85.09 321	93.13 319	88.80 165	96.83 315	88.32 289	79.06 334	94.60 324

MDA-MVSNet-bldmvs		89.97 298	88.35 304	94.83 286	95.21 311	91.34 272	97.64 254	97.51 246	88.36 306	71.17 343	96.13 288	79.22 305	96.63 324	83.65 317	86.27 311	96.52 277

v15		91.94 276	90.77 278	95.43 265	96.31 269	94.83 191	98.77 111	97.50 249	89.92 285	85.13 320	93.08 321	88.76 167	96.86 313	88.40 288	79.10 332	94.61 323

v13		91.88 280	90.69 282	95.43 265	96.33 265	94.78 201	98.75 115	97.50 249	89.68 292	84.93 325	92.98 325	88.84 159	96.83 315	88.14 292	79.09 333	94.69 320

v12		91.89 279	90.70 281	95.43 265	96.31 269	94.80 196	98.76 114	97.50 249	89.76 289	84.95 324	93.00 324	88.82 161	96.82 317	88.23 291	79.00 336	94.68 322

V9		91.91 278	90.73 280	95.45 262	96.32 268	94.80 196	98.77 111	97.50 249	89.81 288	85.03 323	93.08 321	88.76 167	96.86 313	88.24 290	79.03 335	94.69 320

USDC		93.33 257	92.71 256	95.21 273	96.83 233	90.83 279	96.91 292	97.50 249	93.84 157	90.72 285	98.14 145	77.69 311	98.82 204	89.51 270	93.21 237	95.97 297

ITE_SJBPF				95.44 263	97.42 197	91.32 273		97.50 249	95.09 113	93.59 231	98.35 126	81.70 289	98.88 197	89.71 265	93.39 232	96.12 293

Patchmatch-test		94.42 223	93.68 232	96.63 200	97.60 183	91.76 267	94.83 332	97.49 255	89.45 297	94.14 215	97.10 225	88.99 148	98.83 203	85.37 314	98.13 130	99.29 99

YYNet1		90.70 294	89.39 295	94.62 291	94.79 317	90.65 284	97.20 281	97.46 256	87.54 310	72.54 341	95.74 295	86.51 220	96.66 323	86.00 308	86.76 310	96.54 275

MDA-MVSNet_test_wron		90.71 293	89.38 296	94.68 289	94.83 316	90.78 281	97.19 282	97.46 256	87.60 309	72.41 342	95.72 298	86.51 220	96.71 322	85.92 309	86.80 309	96.56 273

BH-RMVSNet		95.92 129	95.32 139	97.69 127	98.32 142	94.64 205	98.19 198	97.45 258	94.56 131	96.03 161	98.61 103	85.02 251	99.12 162	90.68 241	99.06 91	99.30 97

MIMVSNet1		89.67 300	88.28 305	93.82 302	92.81 328	91.08 277	98.01 218	97.45 258	87.95 307	87.90 304	95.87 294	67.63 339	94.56 335	78.73 330	88.18 291	95.83 300

v11		91.85 281	90.68 283	95.36 270	96.34 261	94.74 203	98.80 103	97.43 260	89.60 295	85.09 321	93.03 323	88.53 176	96.75 318	87.37 300	79.96 327	94.58 325

OurMVSNet-221017-0		94.21 232	94.00 210	94.85 284	95.60 301	89.22 300	98.89 78	97.43 260	95.29 102	92.18 271	98.52 113	82.86 284	98.59 219	93.46 173	91.76 251	96.74 242

BH-w/o		95.38 166	95.08 149	96.26 234	98.34 140	91.79 266	97.70 249	97.43 260	92.87 207	94.24 209	97.22 214	88.66 171	98.84 201	91.55 226	97.70 145	98.16 174

VDD-MVS		95.82 133	95.23 143	97.61 137	98.84 113	93.98 229	98.68 133	97.40 263	95.02 115	97.95 73	99.34 20	74.37 328	99.78 77	98.64 4	96.80 157	99.08 123

Gipuma		78.40 317	76.75 318	83.38 331	95.54 303	80.43 334	79.42 351	97.40 263	64.67 345	73.46 340	80.82 346	45.65 350	93.14 341	66.32 345	87.43 299	76.56 351
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_normal		94.72 205	93.59 236	98.11 101	95.30 310	95.95 121	97.91 229	97.39 265	94.64 129	85.70 314	95.88 293	80.52 298	99.36 139	96.69 83	98.30 125	99.01 129

LP		91.12 289	89.99 291	94.53 292	96.35 260	88.70 307	93.86 339	97.35 266	84.88 324	90.98 282	94.77 307	84.40 265	97.43 302	75.41 337	91.89 250	97.47 194

DI_MVS_plusplus_test		94.74 204	93.62 234	98.09 102	95.34 309	95.92 132	98.09 212	97.34 267	94.66 128	85.89 311	95.91 292	80.49 299	99.38 138	96.66 84	98.22 126	98.97 131

PatchFormer-LS_test		95.47 157	95.27 142	96.08 241	97.59 184	90.66 283	98.10 211	97.34 267	93.98 150	96.08 159	96.15 287	87.65 203	99.12 162	95.27 131	95.24 203	98.44 161

tpmp4_e23		93.91 248	93.42 247	95.38 269	97.62 181	88.59 310	97.52 261	97.34 267	87.94 308	94.17 214	96.79 263	82.91 283	99.05 173	90.62 243	95.91 197	98.50 157

new-patchmatchnet		88.50 306	87.45 307	91.67 315	90.31 334	85.89 324	97.16 284	97.33 270	89.47 296	83.63 328	92.77 329	76.38 317	95.06 334	82.70 319	77.29 338	94.06 332

ADS-MVSNet2		94.58 216	94.40 189	95.11 277	98.00 161	88.74 306	96.04 315	97.30 271	90.15 276	96.47 151	96.64 269	87.89 193	97.56 300	90.08 256	97.06 152	99.02 126

MDTV_nov1_ep13			95.40 131		97.48 191	88.34 313	96.85 299	97.29 272	93.74 164	97.48 99	97.26 210	89.18 144	99.05 173	91.92 218	97.43 149

pmmvs5		93.65 253	92.97 252	95.68 254	95.49 305	92.37 258	98.20 194	97.28 273	89.66 293	92.58 259	97.26 210	82.14 286	98.09 278	93.18 181	90.95 258	96.58 269

EPNet_dtu		95.21 178	94.95 156	95.99 242	96.17 278	90.45 287	98.16 203	97.27 274	96.77 44	93.14 247	98.33 131	90.34 129	98.42 247	85.57 311	98.81 103	99.09 120
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous20231206		91.66 284	91.10 272	93.33 306	94.02 323	87.35 320	98.58 146	97.26 275	90.48 270	90.16 289	96.31 279	83.83 280	96.53 325	79.36 327	89.90 265	96.12 293

RPMNet		92.52 268	91.17 271	96.59 205	97.00 221	93.43 243	94.96 328	97.26 275	82.27 332	96.93 114	92.12 336	86.98 214	97.88 292	76.32 334	96.65 160	98.46 159

test_0402		91.32 286	90.27 288	94.48 294	96.60 243	91.12 276	98.50 163	97.22 277	86.10 317	88.30 302	96.98 243	77.65 313	97.99 284	78.13 331	92.94 239	94.34 327

dp		94.15 239	93.90 217	94.90 282	97.31 204	86.82 323	96.97 288	97.19 278	91.22 264	96.02 162	96.61 271	85.51 244	99.02 180	90.00 260	94.30 206	98.85 138

thres200		95.25 175	94.57 180	97.28 157	98.81 114	94.92 174	98.20 194	97.11 279	95.24 106	96.54 138	96.22 285	84.58 258	99.53 126	87.93 297	96.50 166	97.39 198

PatchT		93.06 264	91.97 266	96.35 228	96.69 240	92.67 255	94.48 335	97.08 280	86.62 313	97.08 105	92.23 335	87.94 191	97.90 288	78.89 329	96.69 158	98.49 158

TDRefinement		91.06 290	89.68 293	95.21 273	85.35 343	91.49 271	98.51 162	97.07 281	91.47 248	88.83 300	97.84 168	77.31 315	99.09 170	92.79 195	77.98 337	95.04 313

LF4IMVS		93.14 263	92.79 255	94.20 299	95.88 292	88.67 308	97.66 253	97.07 281	93.81 159	91.71 276	97.65 185	77.96 310	98.81 205	91.47 230	91.92 249	95.12 310

Anonymous20231211		83.69 313	81.50 315	90.26 317	89.23 337	80.10 335	97.97 222	97.06 283	72.79 343	82.05 331	92.57 331	50.28 347	96.32 328	76.15 335	75.38 341	94.37 326

MIMVSNet		93.26 259	92.21 264	96.41 224	97.73 177	93.13 251	95.65 323	97.03 284	91.27 262	94.04 220	96.06 289	75.33 321	97.19 306	86.56 304	96.23 184	98.92 136

EPNet		97.28 82	96.87 83	98.51 76	94.98 313	96.14 111	98.90 74	97.02 285	98.28 1	95.99 163	99.11 49	91.36 114	99.89 29	96.98 65	99.19 88	99.50 74
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TR-MVS		94.94 191	94.20 197	97.17 162	97.75 175	94.14 226	97.59 257	97.02 285	92.28 233	95.75 165	97.64 187	83.88 278	98.96 185	89.77 262	96.15 187	98.40 162

view600		95.60 145	94.93 157	97.62 132	99.05 84	94.85 180	99.09 52	97.01 287	95.36 95	96.52 140	97.37 201	84.55 259	99.59 110	89.07 277	96.39 169	98.40 162

view800		95.60 145	94.93 157	97.62 132	99.05 84	94.85 180	99.09 52	97.01 287	95.36 95	96.52 140	97.37 201	84.55 259	99.59 110	89.07 277	96.39 169	98.40 162

conf0.05thres1000		95.60 145	94.93 157	97.62 132	99.05 84	94.85 180	99.09 52	97.01 287	95.36 95	96.52 140	97.37 201	84.55 259	99.59 110	89.07 277	96.39 169	98.40 162

tfpn		95.60 145	94.93 157	97.62 132	99.05 84	94.85 180	99.09 52	97.01 287	95.36 95	96.52 140	97.37 201	84.55 259	99.59 110	89.07 277	96.39 169	98.40 162

JIA-IIPM		93.35 255	92.49 260	95.92 244	96.48 250	90.65 284	95.01 327	96.96 291	85.93 319	96.08 159	87.33 340	87.70 201	98.78 208	91.35 231	95.58 201	98.34 169

pmmvs-eth3d		90.36 296	89.05 299	94.32 298	91.10 332	92.12 260	97.63 256	96.95 292	88.86 303	84.91 326	93.13 319	78.32 308	96.74 319	88.70 284	81.81 324	94.09 331

tfpn200view9		95.32 173	94.62 178	97.43 150	98.94 97	94.98 170	98.68 133	96.93 293	95.33 99	96.55 136	96.53 272	84.23 271	99.56 119	88.11 293	96.29 177	97.76 185

thres400		95.38 166	94.62 178	97.65 131	98.94 97	94.98 170	98.68 133	96.93 293	95.33 99	96.55 136	96.53 272	84.23 271	99.56 119	88.11 293	96.29 177	98.40 162

tfpn111		95.43 160	94.74 173	97.51 142	98.98 92	94.92 174	98.87 81	96.90 295	95.38 91	96.61 130	96.88 257	84.29 266	99.59 110	88.43 287	96.32 175	98.02 177

conf200view11		95.40 165	94.70 175	97.50 147	98.98 92	94.92 174	98.87 81	96.90 295	95.38 91	96.61 130	96.88 257	84.29 266	99.56 119	88.11 293	96.29 177	98.02 177

thres100view900		95.38 166	94.70 175	97.41 151	98.98 92	94.92 174	98.87 81	96.90 295	95.38 91	96.61 130	96.88 257	84.29 266	99.56 119	88.11 293	96.29 177	97.76 185

thres600view7		95.49 156	94.77 171	97.67 129	98.98 92	95.02 166	98.85 88	96.90 295	95.38 91	96.63 129	96.90 254	84.29 266	99.59 110	88.65 286	96.33 174	98.40 162

CostFormer		94.95 189	94.73 174	95.60 256	97.28 205	89.06 302	97.53 260	96.89 299	89.66 293	96.82 123	96.72 265	86.05 235	98.95 189	95.53 122	96.13 188	98.79 142

new_pmnet		90.06 297	89.00 300	93.22 309	94.18 320	88.32 314	96.42 313	96.89 299	86.19 315	85.67 315	93.62 314	77.18 316	97.10 307	81.61 322	89.29 274	94.23 328

OpenMVS_ROB	86.42 20	89.00 302	87.43 308	93.69 303	93.08 326	89.42 297	97.91 229	96.89 299	78.58 338	85.86 312	94.69 308	69.48 335	98.29 269	77.13 332	93.29 235	93.36 336

tpm2		94.19 234	93.76 227	95.46 261	97.23 208	89.04 303	97.31 277	96.85 302	87.08 312	96.21 157	96.79 263	83.75 281	98.74 209	92.43 206	96.23 184	98.59 154

TransMVSNet (Re)		92.67 266	91.51 270	96.15 237	96.58 244	94.65 204	98.90 74	96.73 303	90.86 268	89.46 295	97.86 165	85.62 242	98.09 278	86.45 305	81.12 325	95.71 303

ambc				89.49 319	86.66 342	75.78 340	92.66 341	96.72 304		86.55 309	92.50 332	46.01 349	97.90 288	90.32 252	82.09 321	94.80 316

LCM-MVSNet		78.70 316	76.24 320	86.08 326	77.26 353	71.99 346	94.34 336	96.72 304	61.62 347	76.53 338	89.33 338	33.91 356	92.78 342	81.85 321	74.60 342	93.46 335

TinyColmap		92.31 270	91.53 269	94.65 290	96.92 226	89.75 292	96.92 290	96.68 306	90.45 272	89.62 293	97.85 167	76.06 319	98.81 205	86.74 303	92.51 242	95.41 308

Baseline_NR-MVSNet		94.35 226	93.81 221	95.96 243	96.20 276	94.05 228	98.61 143	96.67 307	91.44 250	93.85 227	97.60 189	88.57 173	98.14 274	94.39 149	86.93 306	95.68 304

SixPastTwentyTwo		93.34 256	92.86 253	94.75 288	95.67 299	89.41 298	98.75 115	96.67 307	93.89 154	90.15 290	98.25 139	80.87 294	98.27 270	90.90 238	90.64 259	96.57 271

test2356		88.68 305	88.61 301	88.87 320	89.90 336	78.23 336	95.11 326	96.66 309	88.66 305	89.06 298	94.33 313	73.14 331	92.56 343	75.56 336	95.11 204	95.81 301

DWT-MVSNet_test		94.82 197	94.36 190	96.20 236	97.35 202	90.79 280	98.34 178	96.57 310	92.91 205	95.33 169	96.44 277	82.00 287	99.12 162	94.52 147	95.78 200	98.70 146

testus		88.91 303	89.08 298	88.40 321	91.39 330	76.05 339	96.56 308	96.48 311	89.38 299	89.39 296	95.17 304	70.94 333	93.56 339	77.04 333	95.41 202	95.61 305

1111		84.94 312	84.30 313	86.86 324	87.59 339	75.10 341	96.63 305	96.43 312	82.53 330	80.75 334	92.91 327	68.94 336	93.79 337	68.24 343	84.66 318	91.70 338

.test1245		73.05 321	76.31 319	63.27 342	87.59 339	75.10 341	96.63 305	96.43 312	82.53 330	80.75 334	92.91 327	68.94 336	93.79 337	68.24 343	12.72 355	20.91 355

test1235678		86.26 311	85.81 310	87.62 323	86.97 341	75.00 343	96.55 310	96.32 314	86.08 318	81.32 333	92.98 325	73.10 332	92.05 344	71.64 340	87.32 301	95.81 301

LFMVS		95.86 131	94.98 153	98.47 80	98.87 109	96.32 106	98.84 91	96.02 315	93.40 188	98.62 42	99.20 38	74.99 323	99.63 106	97.72 43	97.20 151	99.46 84

IB-MVS	91.98 17	93.27 258	91.97 266	97.19 160	97.47 192	93.41 245	97.09 286	95.99 316	93.32 191	92.47 264	95.73 296	78.06 309	99.53 126	94.59 145	82.98 320	98.62 153
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
test0.0.03 1		94.08 242	93.51 242	95.80 250	95.53 304	92.89 254	97.38 268	95.97 317	95.11 110	92.51 263	96.66 267	87.71 199	96.94 309	87.03 302	93.67 223	97.57 193

FPMVS		77.62 319	77.14 317	79.05 334	79.25 349	60.97 353	95.79 321	95.94 318	65.96 344	67.93 345	94.40 310	37.73 353	88.88 349	68.83 342	88.46 288	87.29 342

Patchmatch-RL test		91.49 285	90.85 277	93.41 305	91.37 331	84.40 325	92.81 340	95.93 319	91.87 241	87.25 305	94.87 306	88.99 148	96.53 325	92.54 203	82.00 322	99.30 97

tpm		94.13 240	93.80 222	95.12 276	96.50 248	87.91 317	97.44 263	95.89 320	92.62 212	96.37 155	96.30 280	84.13 274	98.30 267	93.24 178	91.66 253	99.14 117

conf0.01		95.56 149	94.84 165	97.72 121	98.90 99	95.93 125	99.17 35	95.70 321	93.42 182	96.50 145	97.16 216	86.12 227	99.22 149	90.51 245	96.06 190	98.02 177

conf0.002		95.56 149	94.84 165	97.72 121	98.90 99	95.93 125	99.17 35	95.70 321	93.42 182	96.50 145	97.16 216	86.12 227	99.22 149	90.51 245	96.06 190	98.02 177

thresconf0.02		95.50 152	94.84 165	97.51 142	98.90 99	95.93 125	99.17 35	95.70 321	93.42 182	96.50 145	97.16 216	86.12 227	99.22 149	90.51 245	96.06 190	97.37 200

tfpn_n400		95.50 152	94.84 165	97.51 142	98.90 99	95.93 125	99.17 35	95.70 321	93.42 182	96.50 145	97.16 216	86.12 227	99.22 149	90.51 245	96.06 190	97.37 200

tfpnconf		95.50 152	94.84 165	97.51 142	98.90 99	95.93 125	99.17 35	95.70 321	93.42 182	96.50 145	97.16 216	86.12 227	99.22 149	90.51 245	96.06 190	97.37 200

tfpnview11		95.50 152	94.84 165	97.51 142	98.90 99	95.93 125	99.17 35	95.70 321	93.42 182	96.50 145	97.16 216	86.12 227	99.22 149	90.51 245	96.06 190	97.37 200

LCM-MVSNet-Re		95.22 177	95.32 139	94.91 281	98.18 152	87.85 318	98.75 115	95.66 327	95.11 110	88.96 299	96.85 260	90.26 132	97.65 296	95.65 119	98.44 118	99.22 106

test12356		83.47 314	83.37 314	83.78 330	84.43 344	70.09 348	95.12 325	95.60 328	82.98 328	78.89 336	92.43 334	64.99 341	91.41 346	70.36 341	85.55 317	89.82 340

tfpn1000		95.72 136	95.11 147	97.58 138	99.00 89	95.73 142	99.24 20	95.49 329	94.08 144	96.87 120	97.45 198	85.81 239	99.30 141	91.78 221	96.22 186	97.71 190

tfpn_ndepth		95.53 151	94.90 162	97.39 156	98.96 96	95.88 137	99.05 57	95.27 330	93.80 160	96.95 111	96.93 252	85.53 243	99.40 135	91.54 227	96.10 189	96.89 227

test-LLR		95.10 182	94.87 163	95.80 250	96.77 234	89.70 293	96.91 292	95.21 331	95.11 110	94.83 178	95.72 298	87.71 199	98.97 182	93.06 183	98.50 115	98.72 144

test-mter		94.08 242	93.51 242	95.80 250	96.77 234	89.70 293	96.91 292	95.21 331	92.89 206	94.83 178	95.72 298	77.69 311	98.97 182	93.06 183	98.50 115	98.72 144

PM-MVS		87.77 307	86.55 309	91.40 316	91.03 333	83.36 329	96.92 290	95.18 333	91.28 261	86.48 310	93.42 315	53.27 346	96.74 319	89.43 272	81.97 323	94.11 330

DeepMVS_CX				86.78 325	97.09 219	72.30 345		95.17 334	75.92 340	84.34 327	95.19 302	70.58 334	95.35 331	79.98 326	89.04 277	92.68 337

K. test v3		92.55 267	91.91 268	94.48 294	95.64 300	89.24 299	99.07 56	94.88 335	94.04 146	86.78 307	97.59 190	77.64 314	97.64 297	92.08 210	89.43 272	96.57 271

TESTMET0.1,1		94.18 236	93.69 231	95.63 255	96.92 226	89.12 301	96.91 292	94.78 336	93.17 195	94.88 175	96.45 276	78.52 307	98.92 191	93.09 182	98.50 115	98.85 138

pmmvs3		86.67 310	84.86 312	92.11 314	88.16 338	87.19 322	96.63 305	94.75 337	79.88 337	87.22 306	92.75 330	66.56 340	95.20 333	81.24 323	76.56 340	93.96 333

testmv		78.74 315	77.35 316	82.89 332	78.16 352	69.30 349	95.87 319	94.65 338	81.11 334	70.98 344	87.11 341	46.31 348	90.42 347	65.28 346	76.72 339	88.95 341

door								94.64 339

door-mid								94.37 340

DSMNet-mixed		92.52 268	92.58 259	92.33 312	94.15 321	82.65 331	98.30 186	94.26 341	89.08 302	92.65 257	95.73 296	85.01 252	95.76 330	86.24 306	97.76 143	98.59 154

no-one		74.41 320	70.76 322	85.35 328	79.88 348	76.83 337	94.68 333	94.22 342	80.33 336	63.81 346	79.73 347	35.45 355	93.36 340	71.78 339	36.99 352	85.86 345

MTMP								94.14 343

testpf		88.74 304	89.09 297	87.69 322	95.78 295	83.16 330	84.05 350	94.13 344	85.22 323	90.30 288	94.39 311	74.92 324	95.80 329	89.77 262	93.28 236	84.10 346

PMVS	61.03 23	65.95 325	63.57 327	73.09 339	57.90 357	51.22 358	85.05 349	93.93 345	54.45 349	44.32 353	83.57 342	13.22 359	89.15 348	58.68 350	81.00 326	78.91 350
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		77.95 318	75.44 321	85.46 327	82.54 345	74.95 344	94.23 337	93.08 346	72.80 342	74.68 339	87.38 339	36.36 354	91.56 345	73.95 338	63.94 345	89.87 339

MVS-HIRNet		89.46 301	88.40 303	92.64 310	97.58 185	82.15 332	94.16 338	93.05 347	75.73 341	90.90 283	82.52 343	79.42 304	98.33 262	83.53 318	98.68 105	97.43 195

EPMVS		94.99 185	94.48 183	96.52 215	97.22 209	91.75 268	97.23 280	91.66 348	94.11 142	97.28 100	96.81 262	85.70 241	98.84 201	93.04 185	97.28 150	98.97 131

lessismore_v0				94.45 297	94.93 315	88.44 312		91.03 349		86.77 308	97.64 187	76.23 318	98.42 247	90.31 253	85.64 316	96.51 279

ANet_high		69.08 322	65.37 324	80.22 333	65.99 356	71.96 347	90.91 344	90.09 350	82.62 329	49.93 352	78.39 348	29.36 357	81.75 352	62.49 349	38.52 351	86.95 344

gg-mvs-nofinetune		92.21 271	90.58 285	97.13 164	96.75 237	95.09 164	95.85 320	89.40 351	85.43 322	94.50 186	81.98 344	80.80 296	98.40 260	92.16 208	98.33 123	97.88 183

GG-mvs-BLEND				96.59 205	96.34 261	94.98 170	96.51 312	88.58 352		93.10 249	94.34 312	80.34 301	98.05 280	89.53 269	96.99 154	96.74 242

PNet_i23d		67.70 324	65.07 325	75.60 336	78.61 350	59.61 355	89.14 345	88.24 353	61.83 346	52.37 350	80.89 345	18.91 358	84.91 351	62.70 348	52.93 347	82.28 347

wuykxyi23d		63.73 328	58.86 330	78.35 335	67.62 355	67.90 350	86.56 347	87.81 354	58.26 348	42.49 354	70.28 352	11.55 361	85.05 350	63.66 347	41.50 348	82.11 348

E-PMN		64.94 326	64.25 326	67.02 340	82.28 346	59.36 356	91.83 343	85.63 355	52.69 350	60.22 348	77.28 349	41.06 352	80.12 354	46.15 352	41.14 349	61.57 353

EMVS		64.07 327	63.26 328	66.53 341	81.73 347	58.81 357	91.85 342	84.75 356	51.93 352	59.09 349	75.13 350	43.32 351	79.09 355	42.03 353	39.47 350	61.69 352

tmp_tt		68.90 323	66.97 323	74.68 338	50.78 358	59.95 354	87.13 346	83.47 357	38.80 353	62.21 347	96.23 283	64.70 342	76.91 356	88.91 281	30.49 353	87.19 343

MVE	62.14 22	63.28 329	59.38 329	74.99 337	74.33 354	65.47 351	85.55 348	80.50 358	52.02 351	51.10 351	75.00 351	10.91 363	80.50 353	51.60 351	53.40 346	78.99 349
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
N_pmnet		87.12 309	87.77 306	85.17 329	95.46 306	61.92 352	97.37 270	70.66 359	85.83 320	88.73 301	96.04 290	85.33 249	97.76 295	80.02 324	90.48 260	95.84 299

wuyk23d		30.17 331	30.18 333	30.16 344	78.61 350	43.29 359	66.79 352	14.21 360	17.31 354	14.82 357	11.93 358	11.55 361	41.43 357	37.08 354	19.30 354	5.76 357

testmvs		21.48 333	24.95 334	11.09 346	14.89 359	6.47 361	96.56 308	9.87 361	7.55 355	17.93 355	39.02 354	9.43 364	5.90 359	16.56 356	12.72 355	20.91 355

test123		20.95 334	23.72 335	12.64 345	13.54 360	8.19 360	96.55 310	6.13 362	7.48 356	16.74 356	37.98 355	12.97 360	6.05 358	16.69 355	5.43 357	23.68 354

pcd_1.5k_mvsjas		7.88 336	10.50 337	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	94.51 63	0.00 360	0.00 357	0.00 358	0.00 358

sosnet-low-res		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

sosnet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uncertanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

Regformer		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

n2								0.00 363

nn								0.00 363

ab-mvs-re		8.20 335	10.94 336	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	98.43 118	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

GSMVS																99.20 107

test_part3							98.55 153		96.40 57		99.31 22		99.93 9	96.37 96

test_part2					99.63 21	99.18 1				99.27 7

sam_mvs1												89.45 137				99.20 107

sam_mvs												88.99 148

test_post1							96.68 304				30.43 357	87.85 196	98.69 210	92.59 200

test_post											31.83 356	88.83 160	98.91 192

patchmatchnet-post											95.10 305	89.42 138	98.89 196

gm-plane-assit					95.88 292	87.47 319			89.74 291		96.94 248		99.19 156	93.32 177

test9_res														96.39 95	99.57 58	99.69 38

agg_prior2														95.87 109	99.57 58	99.68 44

test_prior4						98.01 44	97.86 237

test_prior2							97.80 242		96.12 65	97.89 78	98.69 95	95.96 28		96.89 72	99.60 52

旧先验2							97.57 259		91.30 259	98.67 39			99.80 60	95.70 118

新几何2							97.64 254

原ACMM2							97.67 252

testdata2													99.89 29	91.65 225

segment_acmp												96.85 6

testdata1							97.32 276		96.34 59

plane_prior7					97.42 197	94.63 206

plane_prior6					97.35 202	94.61 209						87.09 211

plane_prior4											98.28 134

plane_prior3						94.61 209			97.02 39	95.34 167

plane_prior2							98.80 103		97.28 21

plane_prior1					97.37 201

plane_prior						94.60 211	98.44 168		96.74 46						94.22 209

HQP5-MVS						94.25 224

HQP-NCC					97.20 211		98.05 214		96.43 54	94.45 188

ACMP_Plane					97.20 211		98.05 214		96.43 54	94.45 188

BP-MVS														95.30 128

HQP4-MVS										94.45 188			98.96 185			96.87 230

HQP2-MVS												86.75 217

NP-MVS					97.28 205	94.51 214					97.73 177

MDTV_nov1_ep13_2view						84.26 326	96.89 296		90.97 267	97.90 77		89.89 135		93.91 162		99.18 113

ACMMP++_ref															92.97 238

ACMMP++															93.61 226

Test By Simon												94.64 60

High-resolution multi-view benchmark