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 1	99.97 1	99.97 1	99.95 1	99.74 1	99.98 1	99.56 1	100.00 1	99.85 3

UA-Net		98.88 7	98.76 13	99.22 2	99.11 83	97.89 14	99.47 3	99.32 10	99.08 10	97.87 139	99.67 2	96.47 88	99.92 4	97.88 23	99.98 2	99.85 3

ANet_high		98.31 28	98.94 6	96.41 204	99.33 44	89.64 250	97.92 55	99.56 5	99.27 6	99.66 8	99.50 6	97.67 25	99.83 28	97.55 37	99.98 2	99.77 8

PS-MVSNAJss		98.53 19	98.63 19	98.21 77	99.68 9	94.82 128	98.10 45	99.21 14	96.91 85	99.75 2	99.45 9	95.82 108	99.92 4	98.80 4	99.96 4	99.89 1

mvs_tets		98.90 5	98.94 6	98.75 33	99.69 8	96.48 60	98.54 20	99.22 13	96.23 111	99.71 4	99.48 7	98.77 6	99.93 2	98.89 3	99.95 5	99.84 5

test_djsdf		98.73 11	98.74 16	98.69 40	99.63 12	96.30 66	98.67 13	99.02 52	96.50 100	99.32 20	99.44 10	97.43 31	99.92 4	98.73 7	99.95 5	99.86 2

LTVRE_ROB	96.88 1	99.18 2	99.34 2	98.72 38	99.71 7	96.99 45	99.69 2	99.57 4	99.02 15	99.62 10	99.36 14	98.53 7	99.52 179	98.58 12	99.95 5	99.66 22
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
jajsoiax		98.77 9	98.79 12	98.74 35	99.66 10	96.48 60	98.45 25	99.12 28	95.83 136	99.67 6	99.37 12	98.25 10	99.92 4	98.77 5	99.94 8	99.82 6

v8		97.60 84	98.06 38	96.23 211	98.71 119	89.44 254	97.43 87	98.82 114	97.29 77	98.74 48	99.10 32	93.86 174	99.68 123	98.61 10	99.94 8	99.56 35

Anonymous20240521		97.07 114	97.51 85	95.76 232	99.35 42	88.18 276	97.78 61	98.40 180	97.11 80	98.34 81	99.04 37	89.58 253	99.79 39	98.09 18	99.93 10	99.30 104

test_part1		96.77 136	96.53 146	97.47 136	98.04 194	92.92 194	97.93 53	98.85 94	98.83 21	99.30 21	99.07 35	79.25 315	99.79 39	97.59 35	99.93 10	99.69 20

v7n		98.73 11	98.99 5	97.95 95	99.64 11	94.20 154	98.67 13	99.14 26	99.08 10	99.42 15	99.23 21	96.53 83	99.91 12	99.27 2	99.93 10	99.73 15

PS-CasMVS		98.73 11	98.85 10	98.39 59	99.55 18	95.47 100	98.49 22	99.13 27	99.22 8	99.22 27	98.96 42	97.35 34	99.92 4	97.79 28	99.93 10	99.79 7

UniMVSNet_ETH3D		99.12 3	99.28 3	98.65 43	99.77 3	96.34 64	99.18 5	99.20 16	99.67 2	99.73 3	99.65 4	99.15 3	99.86 20	97.22 46	99.92 14	99.77 8

v10		97.55 87	97.97 41	96.31 208	98.60 134	89.64 250	97.44 85	99.02 52	96.60 94	98.72 50	99.16 29	93.48 183	99.72 85	98.76 6	99.92 14	99.58 28

PEN-MVS		98.75 10	98.85 10	98.44 55	99.58 15	95.67 88	98.45 25	99.15 24	99.33 5	99.30 21	99.00 38	97.27 38	99.92 4	97.64 34	99.92 14	99.75 13

anonymousdsp		98.72 14	98.63 19	98.99 13	99.62 13	97.29 38	98.65 16	99.19 18	95.62 144	99.35 19	99.37 12	97.38 33	99.90 13	98.59 11	99.91 17	99.77 8

FC-MVSNet-test		98.16 33	98.37 27	97.56 123	99.49 27	93.10 190	98.35 28	99.21 14	98.43 29	98.89 39	98.83 50	94.30 164	99.81 32	97.87 24	99.91 17	99.77 8

DTE-MVSNet		98.79 8	98.86 8	98.59 47	99.55 18	96.12 71	98.48 24	99.10 31	99.36 4	99.29 23	99.06 36	97.27 38	99.93 2	97.71 32	99.91 17	99.70 18

CP-MVSNet		98.42 23	98.46 24	98.30 67	99.46 29	95.22 116	98.27 34	98.84 99	99.05 13	99.01 35	98.65 63	95.37 129	99.90 13	97.57 36	99.91 17	99.77 8

WR-MVS_H		98.65 15	98.62 21	98.75 33	99.51 23	96.61 56	98.55 19	99.17 19	99.05 13	99.17 29	98.79 51	95.47 126	99.89 16	97.95 21	99.91 17	99.75 13

pmmvs6		99.07 4	99.24 4	98.56 49	99.81 2	96.38 62	98.87 9	99.30 11	99.01 16	99.63 9	99.66 3	99.27 2	99.68 123	97.75 30	99.89 22	99.62 25

OurMVSNet-221017-0		98.61 16	98.61 23	98.63 45	99.77 3	96.35 63	99.17 6	99.05 43	98.05 41	99.61 11	99.52 5	93.72 179	99.88 18	98.72 9	99.88 23	99.65 23

DeepC-MVS	95.41 4	97.82 69	97.70 62	98.16 78	98.78 110	95.72 83	96.23 147	99.02 52	93.92 208	98.62 52	98.99 39	97.69 23	99.62 148	96.18 78	99.87 24	99.15 137
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Anonymous20231211		98.55 17	98.76 13	97.94 96	98.79 108	94.37 146	98.84 10	99.15 24	99.37 3	99.67 6	99.43 11	95.61 120	99.72 85	98.12 16	99.86 25	99.73 15

TranMVSNet+NR-MVSNet		98.33 26	98.30 31	98.43 56	99.07 87	95.87 79	96.73 125	99.05 43	98.67 24	98.84 42	98.45 76	97.58 28	99.88 18	96.45 71	99.86 25	99.54 38

nrg030		98.54 18	98.62 21	98.32 64	99.22 58	95.66 89	97.90 56	99.08 37	98.31 33	99.02 34	98.74 55	97.68 24	99.61 155	97.77 29	99.85 27	99.70 18

pmmvs-eth3d		96.49 153	96.18 162	97.42 144	98.25 171	94.29 148	94.77 237	98.07 226	89.81 281	97.97 127	98.33 84	93.11 189	99.08 277	95.46 120	99.84 28	98.89 189

FIs		97.93 55	98.07 36	97.48 135	99.38 39	92.95 193	98.03 50	99.11 29	98.04 42	98.62 52	98.66 61	93.75 178	99.78 43	97.23 45	99.84 28	99.73 15

D2MVS		95.18 206	95.17 195	95.21 253	97.76 237	87.76 288	94.15 260	97.94 231	89.77 282	96.99 188	97.68 171	87.45 277	99.14 268	95.03 151	99.81 30	98.74 209

WR-MVS		96.90 125	96.81 128	97.16 158	98.56 139	92.20 209	94.33 249	98.12 218	97.34 74	98.20 97	97.33 203	92.81 196	99.75 65	94.79 159	99.81 30	99.54 38

test_0402		97.84 66	97.97 41	97.47 136	99.19 68	94.07 157	96.71 126	98.73 129	98.66 25	98.56 59	98.41 78	96.84 69	99.69 116	94.82 157	99.81 30	98.64 218

bset_n11_16_dypcd		94.53 239	93.95 250	96.25 210	97.56 256	89.85 247	88.52 355	91.32 347	94.90 176	97.51 152	96.38 264	82.34 304	99.78 43	97.22 46	99.80 33	99.12 148

MIMVSNet1		98.51 20	98.45 26	98.67 41	99.72 6	96.71 51	98.76 11	98.89 79	98.49 28	99.38 17	99.14 30	95.44 128	99.84 25	96.47 70	99.80 33	99.47 61

VPA-MVSNet		98.27 29	98.46 24	97.70 114	99.06 88	93.80 168	97.76 64	99.00 60	98.40 30	99.07 33	98.98 40	96.89 64	99.75 65	97.19 51	99.79 35	99.55 37

Baseline_NR-MVSNet		97.72 76	97.79 55	97.50 131	99.56 16	93.29 184	95.44 190	98.86 90	98.20 38	98.37 75	99.24 20	94.69 149	99.55 170	95.98 91	99.79 35	99.65 23

IterMVS-LS		96.92 123	97.29 98	95.79 231	98.51 144	88.13 279	95.10 215	98.66 149	96.99 82	98.46 68	98.68 60	92.55 205	99.74 75	96.91 60	99.79 35	99.50 45
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
RRT_MVS		94.90 217	94.07 244	97.39 147	93.18 358	93.21 187	95.26 207	97.49 260	93.94 207	98.25 93	97.85 151	72.96 350	99.84 25	97.90 22	99.78 38	99.14 140

NR-MVSNet		97.96 46	97.86 50	98.26 69	98.73 114	95.54 93	98.14 43	98.73 129	97.79 46	99.42 15	97.83 153	94.40 162	99.78 43	95.91 94	99.76 39	99.46 63

SixPastTwentyTwo		97.49 92	97.57 81	97.26 155	99.56 16	92.33 203	98.28 32	96.97 279	98.30 34	99.45 14	99.35 16	88.43 266	99.89 16	98.01 20	99.76 39	99.54 38

FMVSNet1		97.95 50	98.08 35	97.56 123	99.14 81	93.67 173	98.23 35	98.66 149	97.41 72	99.00 36	99.19 24	95.47 126	99.73 81	95.83 99	99.76 39	99.30 104

TDRefinement		98.90 5	98.86 8	99.02 9	99.54 20	98.06 8	99.34 4	99.44 8	98.85 20	99.00 36	99.20 23	97.42 32	99.59 157	97.21 48	99.76 39	99.40 83

pm-mvs1		98.47 21	98.67 17	97.86 102	99.52 22	94.58 138	98.28 32	99.00 60	97.57 61	99.27 24	99.22 22	98.32 9	99.50 184	97.09 54	99.75 43	99.50 45

UniMVSNet (Re)		97.83 67	97.65 69	98.35 63	98.80 107	95.86 80	95.92 167	99.04 49	97.51 66	98.22 96	97.81 157	94.68 151	99.78 43	97.14 53	99.75 43	99.41 82

LPG-MVS_test		97.94 52	97.67 66	98.74 35	99.15 73	97.02 43	97.09 105	99.02 52	95.15 164	98.34 81	98.23 103	97.91 17	99.70 108	94.41 174	99.73 45	99.50 45

LGP-MVS_train				98.74 35	99.15 73	97.02 43		99.02 52	95.15 164	98.34 81	98.23 103	97.91 17	99.70 108	94.41 174	99.73 45	99.50 45

CSCG		97.40 99	97.30 97	97.69 116	98.95 97	94.83 127	97.28 93	98.99 63	96.35 107	98.13 107	95.95 286	95.99 101	99.66 134	94.36 180	99.73 45	98.59 224

IS-MVSNet		96.93 122	96.68 135	97.70 114	99.25 52	94.00 160	98.57 17	96.74 288	98.36 31	98.14 106	97.98 135	88.23 268	99.71 99	93.10 218	99.72 48	99.38 87

ACMH+	93.58 10	98.23 32	98.31 29	97.98 94	99.39 38	95.22 116	97.55 77	99.20 16	98.21 37	99.25 25	98.51 72	98.21 11	99.40 215	94.79 159	99.72 48	99.32 98

CLD-MVS		95.47 194	95.07 199	96.69 186	98.27 168	92.53 200	91.36 325	98.67 147	91.22 268	95.78 248	94.12 325	95.65 119	98.98 289	90.81 258	99.72 48	98.57 225
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_NR-MVSNet		97.83 67	97.65 69	98.37 60	98.72 116	95.78 81	95.66 180	99.02 52	98.11 40	98.31 88	97.69 170	94.65 153	99.85 22	97.02 57	99.71 51	99.48 58

DU-MVS		97.79 71	97.60 78	98.36 61	98.73 114	95.78 81	95.65 183	98.87 87	97.57 61	98.31 88	97.83 153	94.69 149	99.85 22	97.02 57	99.71 51	99.46 63

ACMH	93.61 9	98.44 22	98.76 13	97.51 128	99.43 33	93.54 179	98.23 35	99.05 43	97.40 73	99.37 18	99.08 34	98.79 5	99.47 191	97.74 31	99.71 51	99.50 45
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	92.54 13	97.47 94	97.10 111	98.55 50	99.04 92	96.70 52	96.24 146	98.89 79	93.71 213	97.97 127	97.75 162	97.44 30	99.63 140	93.22 215	99.70 54	99.32 98
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v2v482		96.78 135	97.06 115	95.95 224	98.57 138	88.77 267	95.36 198	98.26 196	95.18 163	97.85 141	98.23 103	92.58 204	99.63 140	97.80 27	99.69 55	99.45 68

UGNet		96.81 133	96.56 142	97.58 122	96.64 299	93.84 167	97.75 65	97.12 273	96.47 103	93.62 306	98.88 47	93.22 188	99.53 175	95.61 110	99.69 55	99.36 93
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
wuyk23d		93.25 274	95.20 193	87.40 348	96.07 319	95.38 103	97.04 108	94.97 315	95.33 156	99.70 5	98.11 117	98.14 13	91.94 366	77.76 359	99.68 57	74.89 366

Vis-MVSNet (Re-imp)		95.11 209	94.85 210	95.87 229	99.12 82	89.17 258	97.54 82	94.92 316	96.50 100	96.58 209	97.27 207	83.64 300	99.48 188	88.42 304	99.67 58	98.97 172

COLMAP_ROB	94.48 6	98.25 31	98.11 34	98.64 44	99.21 65	97.35 36	97.96 51	99.16 20	98.34 32	98.78 45	98.52 71	97.32 35	99.45 198	94.08 189	99.67 58	99.13 143
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test20.03		96.58 150	96.61 137	96.48 199	98.49 148	91.72 221	95.68 179	97.69 247	96.81 88	98.27 92	97.92 144	94.18 168	98.71 313	90.78 260	99.66 60	99.00 168

KD-MVS_self_test		97.86 65	98.07 36	97.25 156	99.22 58	92.81 196	97.55 77	98.94 74	97.10 81	98.85 41	98.88 47	95.03 140	99.67 128	97.39 43	99.65 61	99.26 117

CHOSEN 1792x2688		94.10 252	93.41 259	96.18 215	99.16 70	90.04 243	92.15 314	98.68 144	79.90 352	96.22 229	97.83 153	87.92 274	99.42 204	89.18 293	99.65 61	99.08 157

XVG-ACMP-BASELINE		97.58 86	97.28 100	98.49 52	99.16 70	96.90 47	96.39 135	98.98 66	95.05 169	98.06 116	98.02 130	95.86 104	99.56 166	94.37 177	99.64 63	99.00 168

DROMVSNet		97.90 60	97.94 44	97.79 106	98.66 125	95.14 119	98.31 31	99.66 2	97.57 61	95.95 239	97.01 225	96.99 55	99.82 29	97.66 33	99.64 63	98.39 237

CP-MVS		97.92 56	97.56 82	98.99 13	98.99 95	97.82 16	97.93 53	98.96 71	96.11 115	96.89 196	97.45 188	96.85 68	99.78 43	95.19 136	99.63 65	99.38 87

test_0728_THIRD									96.62 92	98.40 72	98.28 95	97.10 45	99.71 99	95.70 101	99.62 66	99.58 28

tfpnnormal		97.72 76	97.97 41	96.94 170	99.26 49	92.23 206	97.83 60	98.45 170	98.25 35	99.13 30	98.66 61	96.65 75	99.69 116	93.92 198	99.62 66	98.91 185

MP-MVS-pluss		97.69 78	97.36 94	98.70 39	99.50 26	96.84 48	95.38 197	98.99 63	92.45 249	98.11 108	98.31 86	97.25 41	99.77 53	96.60 63	99.62 66	99.48 58
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
v1144		96.84 128	97.08 113	96.13 217	98.42 156	89.28 257	95.41 194	98.67 147	94.21 198	97.97 127	98.31 86	93.06 190	99.65 135	98.06 19	99.62 66	99.45 68

HPM-MVS_fast		98.32 27	98.13 33	98.88 24	99.54 20	97.48 31	98.35 28	99.03 50	95.88 131	97.88 136	98.22 106	98.15 12	99.74 75	96.50 69	99.62 66	99.42 80

Patchmtry		95.03 214	94.59 226	96.33 206	94.83 341	90.82 233	96.38 137	97.20 268	96.59 95	97.49 155	98.57 66	77.67 323	99.38 223	92.95 221	99.62 66	98.80 201

zzz-MVS		98.01 44	97.66 67	99.06 4	99.44 31	97.90 12	95.66 180	98.73 129	97.69 57	97.90 133	97.96 136	95.81 112	99.82 29	96.13 79	99.61 72	99.45 68

MTAPA		98.14 34	97.84 51	99.06 4	99.44 31	97.90 12	97.25 94	98.73 129	97.69 57	97.90 133	97.96 136	95.81 112	99.82 29	96.13 79	99.61 72	99.45 68

Patchmatch-RL test		94.66 232	94.49 230	95.19 255	98.54 141	88.91 262	92.57 306	98.74 127	91.46 263	98.32 86	97.75 162	77.31 328	98.81 304	96.06 82	99.61 72	97.85 284

CANet		95.86 180	95.65 183	96.49 198	96.41 305	90.82 233	94.36 248	98.41 178	94.94 173	92.62 332	96.73 243	92.68 200	99.71 99	95.12 146	99.60 75	98.94 176

FMVSNet2		96.72 140	96.67 136	96.87 175	97.96 204	91.88 217	97.15 100	98.06 227	95.59 147	98.50 64	98.62 64	89.51 257	99.65 135	94.99 153	99.60 75	99.07 159

SteuartSystems-ACMMP		98.02 43	97.76 59	98.79 31	99.43 33	97.21 42	97.15 100	98.90 78	96.58 96	98.08 114	97.87 150	97.02 53	99.76 58	95.25 133	99.59 77	99.40 83
Skip Steuart: Steuart Systems R&D Blog.
USDC		94.56 237	94.57 229	94.55 283	97.78 235	86.43 308	92.75 302	98.65 154	85.96 316	96.91 195	97.93 143	90.82 236	98.74 310	90.71 265	99.59 77	98.47 232

ACMMP_NAP		97.89 61	97.63 74	98.67 41	99.35 42	96.84 48	96.36 138	98.79 116	95.07 168	97.88 136	98.35 82	97.24 42	99.72 85	96.05 84	99.58 79	99.45 68

v1192		96.83 131	97.06 115	96.15 216	98.28 166	89.29 256	95.36 198	98.77 121	93.73 212	98.11 108	98.34 83	93.02 194	99.67 128	98.35 14	99.58 79	99.50 45

APDe-MVS		98.14 34	98.03 40	98.47 54	98.72 116	96.04 74	98.07 47	99.10 31	95.96 125	98.59 57	98.69 59	96.94 58	99.81 32	96.64 62	99.58 79	99.57 32

DPE-MVS		97.64 80	97.35 95	98.50 51	98.85 103	96.18 68	95.21 212	98.99 63	95.84 135	98.78 45	98.08 119	96.84 69	99.81 32	93.98 196	99.57 82	99.52 42
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HPM-MVS		98.11 38	97.83 53	98.92 22	99.42 35	97.46 32	98.57 17	99.05 43	95.43 154	97.41 164	97.50 184	97.98 15	99.79 39	95.58 113	99.57 82	99.50 45
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		98.05 41	97.75 61	98.93 21	99.23 55	97.60 23	98.09 46	98.96 71	95.75 140	97.91 132	98.06 126	96.89 64	99.76 58	95.32 129	99.57 82	99.43 79
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
cl____		94.73 224	94.64 220	95.01 261	95.85 323	87.00 300	91.33 327	98.08 222	93.34 222	97.10 178	97.33 203	84.01 299	99.30 243	95.14 143	99.56 85	98.71 214

miper_lstm_enhance		94.81 222	94.80 214	94.85 269	96.16 315	86.45 307	91.14 333	98.20 203	93.49 217	97.03 185	97.37 200	84.97 292	99.26 252	95.28 131	99.56 85	98.83 198

v144192		96.69 143	96.90 125	96.03 219	98.25 171	88.92 261	95.49 188	98.77 121	93.05 235	98.09 112	98.29 94	92.51 209	99.70 108	98.11 17	99.56 85	99.47 61

EI-MVSNet		96.63 147	96.93 122	95.74 233	97.26 280	88.13 279	95.29 205	97.65 252	96.99 82	97.94 130	98.19 108	92.55 205	99.58 159	96.91 60	99.56 85	99.50 45

K. test v3		96.44 156	96.28 157	96.95 169	99.41 36	91.53 223	97.65 70	90.31 357	98.89 19	98.93 38	99.36 14	84.57 295	99.92 4	97.81 26	99.56 85	99.39 85

MVSTER		94.21 248	93.93 251	95.05 260	95.83 324	86.46 306	95.18 213	97.65 252	92.41 250	97.94 130	98.00 134	72.39 351	99.58 159	96.36 73	99.56 85	99.12 148

DIV-MVS_self_test		94.73 224	94.64 220	95.01 261	95.86 322	87.00 300	91.33 327	98.08 222	93.34 222	97.10 178	97.34 202	84.02 298	99.31 240	95.15 142	99.55 91	98.72 212

v1921920		96.72 140	96.96 121	95.99 220	98.21 175	88.79 266	95.42 192	98.79 116	93.22 227	98.19 100	98.26 100	92.68 200	99.70 108	98.34 15	99.55 91	99.49 53

ACMMP++															99.55 91

SMA-MVS		97.48 93	97.11 110	98.60 46	98.83 104	96.67 53	96.74 121	98.73 129	91.61 260	98.48 65	98.36 81	96.53 83	99.68 123	95.17 138	99.54 94	99.45 68
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
SD-MVS		97.37 101	97.70 62	96.35 205	98.14 187	95.13 120	96.54 130	98.92 76	95.94 127	99.19 28	98.08 119	97.74 22	95.06 364	95.24 134	99.54 94	98.87 195
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
ACMM	93.33 11	98.05 41	97.79 55	98.85 25	99.15 73	97.55 27	96.68 127	98.83 106	95.21 160	98.36 78	98.13 113	98.13 14	99.62 148	96.04 85	99.54 94	99.39 85
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ZNCC-MVS		97.92 56	97.62 76	98.83 26	99.32 46	97.24 40	97.45 84	98.84 99	95.76 138	96.93 193	97.43 190	97.26 40	99.79 39	96.06 82	99.53 97	99.45 68

Anonymous20231206		95.27 203	95.06 201	95.88 228	98.72 116	89.37 255	95.70 176	97.85 236	88.00 300	96.98 190	97.62 174	91.95 221	99.34 233	89.21 292	99.53 97	98.94 176

V42		97.04 115	97.16 108	96.68 188	98.59 136	91.05 228	96.33 140	98.36 185	94.60 184	97.99 123	98.30 90	93.32 185	99.62 148	97.40 42	99.53 97	99.38 87

EU-MVSNet		94.25 245	94.47 231	93.60 299	98.14 187	82.60 341	97.24 96	92.72 337	85.08 328	98.48 65	98.94 43	82.59 303	98.76 309	97.47 40	99.53 97	99.44 78

TransMVSNet (Re)		98.38 25	98.67 17	97.51 128	99.51 23	93.39 183	98.20 40	98.87 87	98.23 36	99.48 12	99.27 19	98.47 8	99.55 170	96.52 67	99.53 97	99.60 26

DVP-MVS		97.78 72	97.65 69	98.16 78	99.24 53	95.51 95	96.74 121	98.23 199	95.92 128	98.40 72	98.28 95	97.06 50	99.71 99	95.48 117	99.52 102	99.26 117
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				98.25 72	99.23 55	95.49 99	96.74 121	98.89 79					99.75 65	95.48 117	99.52 102	99.53 41

v148		96.58 150	96.97 119	95.42 247	98.63 130	87.57 290	95.09 217	97.90 233	95.91 130	98.24 95	97.96 136	93.42 184	99.39 220	96.04 85	99.52 102	99.29 111

EI-MVSNet-UG-set		97.32 105	97.40 91	97.09 163	97.34 275	92.01 215	95.33 201	97.65 252	97.74 51	98.30 90	98.14 112	95.04 139	99.69 116	97.55 37	99.52 102	99.58 28

ACMMP++_ref															99.52 102

MSC_two_6792asdad				98.22 74	97.75 239	95.34 108		98.16 212					99.75 65	95.87 97	99.51 107	99.57 32

No_MVS				98.22 74	97.75 239	95.34 108		98.16 212					99.75 65	95.87 97	99.51 107	99.57 32

SED-MVS		97.94 52	97.90 45	98.07 86	99.22 58	95.35 106	96.79 118	98.83 106	96.11 115	99.08 31	98.24 101	97.87 20	99.72 85	95.44 121	99.51 107	99.14 140

IU-MVS					99.22 58	95.40 101		98.14 215	85.77 320	98.36 78				95.23 135	99.51 107	99.49 53

EI-MVSNet-Vis-set		97.32 105	97.39 92	97.11 161	97.36 270	92.08 213	95.34 200	97.65 252	97.74 51	98.29 91	98.11 117	95.05 137	99.68 123	97.50 39	99.50 111	99.56 35

abl_6		98.42 23	98.19 32	99.09 3	99.16 70	98.10 6	97.73 68	99.11 29	97.76 50	98.62 52	98.27 99	97.88 19	99.80 38	95.67 104	99.50 111	99.38 87

mPP-MVS		97.91 59	97.53 83	99.04 7	99.22 58	97.87 15	97.74 66	98.78 120	96.04 120	97.10 178	97.73 165	96.53 83	99.78 43	95.16 140	99.50 111	99.46 63

Gipuma		98.07 40	98.31 29	97.36 149	99.76 5	96.28 67	98.51 21	99.10 31	98.76 23	96.79 198	99.34 17	96.61 78	98.82 302	96.38 72	99.50 111	96.98 311
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_241102_TWO								98.83 106	96.11 115	98.62 52	98.24 101	96.92 62	99.72 85	95.44 121	99.49 115	99.49 53

v1240		96.74 137	97.02 118	95.91 227	98.18 180	88.52 269	95.39 196	98.88 85	93.15 233	98.46 68	98.40 80	92.80 197	99.71 99	98.45 13	99.49 115	99.49 53

VDD-MVS		97.37 101	97.25 101	97.74 110	98.69 123	94.50 142	97.04 108	95.61 308	98.59 26	98.51 62	98.72 56	92.54 207	99.58 159	96.02 87	99.49 115	99.12 148

PVSNet_BlendedMVS		95.02 215	94.93 206	95.27 251	97.79 231	87.40 294	94.14 262	98.68 144	88.94 289	94.51 278	98.01 132	93.04 191	99.30 243	89.77 285	99.49 115	99.11 152

MP-MVS		97.64 80	97.18 107	99.00 12	99.32 46	97.77 18	97.49 83	98.73 129	96.27 108	95.59 254	97.75 162	96.30 96	99.78 43	93.70 206	99.48 119	99.45 68
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
EPNet		93.72 261	92.62 278	97.03 167	87.61 373	92.25 205	96.27 142	91.28 348	96.74 90	87.65 360	97.39 196	85.00 291	99.64 138	92.14 228	99.48 119	99.20 129
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet_DTU		94.65 233	94.21 240	95.96 222	95.90 321	89.68 249	93.92 272	97.83 240	93.19 228	90.12 349	95.64 294	88.52 264	99.57 165	93.27 214	99.47 121	98.62 221

PMMVS2		93.66 264	94.07 244	92.45 324	97.57 254	80.67 350	86.46 358	96.00 298	93.99 205	97.10 178	97.38 198	89.90 250	97.82 352	88.76 298	99.47 121	98.86 196

baseline		97.44 96	97.78 58	96.43 201	98.52 143	90.75 236	96.84 115	99.03 50	96.51 99	97.86 140	98.02 130	96.67 74	99.36 228	97.09 54	99.47 121	99.19 130

HFP-MVS		97.94 52	97.64 72	98.83 26	99.15 73	97.50 29	97.59 74	98.84 99	96.05 118	97.49 155	97.54 179	97.07 48	99.70 108	95.61 110	99.46 124	99.30 104

#test#		97.62 82	97.22 105	98.83 26	99.15 73	97.50 29	96.81 117	98.84 99	94.25 197	97.49 155	97.54 179	97.07 48	99.70 108	94.37 177	99.46 124	99.30 104

ACMMPR		97.95 50	97.62 76	98.94 18	99.20 66	97.56 26	97.59 74	98.83 106	96.05 118	97.46 161	97.63 173	96.77 71	99.76 58	95.61 110	99.46 124	99.49 53

PGM-MVS		97.88 62	97.52 84	98.96 16	99.20 66	97.62 22	97.09 105	99.06 41	95.45 152	97.55 149	97.94 141	97.11 44	99.78 43	94.77 162	99.46 124	99.48 58

PM-MVS		97.36 103	97.10 111	98.14 82	98.91 100	96.77 50	96.20 148	98.63 155	93.82 210	98.54 60	98.33 84	93.98 172	99.05 280	95.99 90	99.45 128	98.61 223

GeoE		97.75 74	97.70 62	97.89 99	98.88 102	94.53 139	97.10 104	98.98 66	95.75 140	97.62 147	97.59 176	97.61 27	99.77 53	96.34 74	99.44 129	99.36 93

OPM-MVS		97.54 88	97.25 101	98.41 57	99.11 83	96.61 56	95.24 210	98.46 169	94.58 187	98.10 111	98.07 121	97.09 47	99.39 220	95.16 140	99.44 129	99.21 126
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EG-PatchMatch MVS		97.69 78	97.79 55	97.40 146	99.06 88	93.52 180	95.96 163	98.97 70	94.55 188	98.82 43	98.76 54	97.31 36	99.29 247	97.20 50	99.44 129	99.38 87

GBi-Net		96.99 117	96.80 129	97.56 123	97.96 204	93.67 173	98.23 35	98.66 149	95.59 147	97.99 123	99.19 24	89.51 257	99.73 81	94.60 166	99.44 129	99.30 104

test1		96.99 117	96.80 129	97.56 123	97.96 204	93.67 173	98.23 35	98.66 149	95.59 147	97.99 123	99.19 24	89.51 257	99.73 81	94.60 166	99.44 129	99.30 104

FMVSNet3		95.26 204	94.94 204	96.22 213	96.53 302	90.06 242	95.99 160	97.66 250	94.11 202	97.99 123	97.91 145	80.22 313	99.63 140	94.60 166	99.44 129	98.96 173

DP-MVS		97.87 63	97.89 48	97.81 105	98.62 131	94.82 128	97.13 103	98.79 116	98.98 17	98.74 48	98.49 73	95.80 114	99.49 185	95.04 149	99.44 129	99.11 152

TAMVS		95.49 191	94.94 204	97.16 158	98.31 162	93.41 182	95.07 220	96.82 284	91.09 269	97.51 152	97.82 156	89.96 249	99.42 204	88.42 304	99.44 129	98.64 218

region2R		97.92 56	97.59 79	98.92 22	99.22 58	97.55 27	97.60 73	98.84 99	96.00 123	97.22 168	97.62 174	96.87 67	99.76 58	95.48 117	99.43 137	99.46 63

XXY-MVS		97.54 88	97.70 62	97.07 164	99.46 29	92.21 207	97.22 97	99.00 60	94.93 175	98.58 58	98.92 45	97.31 36	99.41 213	94.44 172	99.43 137	99.59 27

PHI-MVS		96.96 121	96.53 146	98.25 72	97.48 261	96.50 59	96.76 120	98.85 94	93.52 216	96.19 231	96.85 233	95.94 102	99.42 204	93.79 202	99.43 137	98.83 198

AllTest		97.20 112	96.92 123	98.06 88	99.08 85	96.16 69	97.14 102	99.16 20	94.35 193	97.78 145	98.07 121	95.84 105	99.12 270	91.41 243	99.42 140	98.91 185

TestCases				98.06 88	99.08 85	96.16 69		99.16 20	94.35 193	97.78 145	98.07 121	95.84 105	99.12 270	91.41 243	99.42 140	98.91 185

Regformer-3		97.25 109	97.29 98	97.11 161	97.35 271	92.32 204	95.26 207	97.62 257	97.67 59	98.17 101	97.89 146	95.05 137	99.56 166	97.16 52	99.42 140	99.46 63

Regformer-4		97.53 90	97.47 90	97.71 112	97.35 271	93.91 162	95.26 207	98.14 215	97.97 43	98.34 81	97.89 146	95.49 123	99.71 99	97.41 41	99.42 140	99.51 44

TinyColmap		96.00 174	96.34 155	94.96 263	97.90 210	87.91 282	94.13 263	98.49 167	94.41 190	98.16 102	97.76 159	96.29 97	98.68 318	90.52 272	99.42 140	98.30 250

3Dnovator	96.53 2	97.61 83	97.64 72	97.50 131	97.74 242	93.65 177	98.49 22	98.88 85	96.86 87	97.11 177	98.55 69	95.82 108	99.73 81	95.94 92	99.42 140	99.13 143

DeepPCF-MVS	94.58 5	96.90 125	96.43 152	98.31 66	97.48 261	97.23 41	92.56 307	98.60 157	92.84 244	98.54 60	97.40 192	96.64 77	98.78 306	94.40 176	99.41 146	98.93 180

CS-MVS		95.98 175	96.24 158	95.20 254	97.26 280	89.88 246	95.84 171	99.39 9	93.89 209	94.28 283	95.15 304	94.81 146	99.62 148	96.11 81	99.40 147	96.10 336

EPP-MVSNet		96.84 128	96.58 140	97.65 118	99.18 69	93.78 170	98.68 12	96.34 292	97.91 45	97.30 166	98.06 126	88.46 265	99.85 22	93.85 200	99.40 147	99.32 98

xxxxxxxxxxxxxcwj		97.24 110	97.03 117	97.89 99	98.48 150	94.71 132	94.53 245	99.07 40	95.02 171	97.83 142	97.88 148	96.44 90	99.72 85	94.59 169	99.39 149	99.25 121

SF-MVS		97.60 84	97.39 92	98.22 74	98.93 98	95.69 85	97.05 107	99.10 31	95.32 157	97.83 142	97.88 148	96.44 90	99.72 85	94.59 169	99.39 149	99.25 121

casdiffmvs		97.50 91	97.81 54	96.56 195	98.51 144	91.04 229	95.83 172	99.09 36	97.23 78	98.33 85	98.30 90	97.03 52	99.37 226	96.58 65	99.38 151	99.28 112

XVS		97.96 46	97.63 74	98.94 18	99.15 73	97.66 20	97.77 62	98.83 106	97.42 69	96.32 222	97.64 172	96.49 86	99.72 85	95.66 106	99.37 152	99.45 68

X-MVStestdata		92.86 278	90.83 303	98.94 18	99.15 73	97.66 20	97.77 62	98.83 106	97.42 69	96.32 222	36.50 368	96.49 86	99.72 85	95.66 106	99.37 152	99.45 68

lessismore_v0				97.05 165	99.36 41	92.12 211		84.07 369		98.77 47	98.98 40	85.36 289	99.74 75	97.34 44	99.37 152	99.30 104

Anonymous20240529		97.96 46	98.04 39	97.71 112	98.69 123	94.28 151	97.86 58	98.31 193	98.79 22	99.23 26	98.86 49	95.76 115	99.61 155	95.49 114	99.36 155	99.23 124

c3_l		95.20 205	95.32 191	94.83 271	96.19 313	86.43 308	91.83 320	98.35 189	93.47 218	97.36 165	97.26 208	88.69 263	99.28 249	95.41 127	99.36 155	98.78 204

FMVSNet5		93.39 270	92.35 281	96.50 197	95.83 324	90.81 235	97.31 91	98.27 194	92.74 245	96.27 226	98.28 95	62.23 366	99.67 128	90.86 256	99.36 155	99.03 165

Vis-MVSNet		98.27 29	98.34 28	98.07 86	99.33 44	95.21 118	98.04 48	99.46 7	97.32 75	97.82 144	99.11 31	96.75 72	99.86 20	97.84 25	99.36 155	99.15 137
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PMVS	89.60 17	96.71 142	96.97 119	95.95 224	99.51 23	97.81 17	97.42 88	97.49 260	97.93 44	95.95 239	98.58 65	96.88 66	96.91 358	89.59 287	99.36 155	93.12 358
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
GST-MVS		97.82 69	97.49 88	98.81 29	99.23 55	97.25 39	97.16 99	98.79 116	95.96 125	97.53 150	97.40 192	96.93 60	99.77 53	95.04 149	99.35 160	99.42 80

ambc				96.56 195	98.23 174	91.68 222	97.88 57	98.13 217		98.42 71	98.56 68	94.22 167	99.04 281	94.05 193	99.35 160	98.95 174

APD-MVS		97.00 116	96.53 146	98.41 57	98.55 140	96.31 65	96.32 141	98.77 121	92.96 242	97.44 163	97.58 178	95.84 105	99.74 75	91.96 230	99.35 160	99.19 130
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ETH3D-3000-0.1		96.89 127	96.46 151	98.16 78	98.62 131	95.69 85	95.96 163	98.98 66	93.36 221	97.04 184	97.31 205	94.93 144	99.63 140	92.60 222	99.34 163	99.17 133

MVS_0304		95.50 190	95.05 202	96.84 177	96.28 308	93.12 189	97.00 110	96.16 294	95.03 170	89.22 354	97.70 168	90.16 248	99.48 188	94.51 171	99.34 163	97.93 281

jason		94.39 243	94.04 246	95.41 249	98.29 164	87.85 285	92.74 304	96.75 287	85.38 327	95.29 259	96.15 274	88.21 269	99.65 135	94.24 183	99.34 163	98.74 209
jason: jason.
CPTT-MVS		96.69 143	96.08 167	98.49 52	98.89 101	96.64 55	97.25 94	98.77 121	92.89 243	96.01 238	97.13 213	92.23 213	99.67 128	92.24 227	99.34 163	99.17 133

MVS_111021_LR		96.82 132	96.55 143	97.62 120	98.27 168	95.34 108	93.81 277	98.33 190	94.59 186	96.56 211	96.63 249	96.61 78	98.73 311	94.80 158	99.34 163	98.78 204

OMC-MVS		96.48 154	96.00 170	97.91 98	98.30 163	96.01 77	94.86 232	98.60 157	91.88 257	97.18 172	97.21 211	96.11 99	99.04 281	90.49 275	99.34 163	98.69 215

DeepC-MVS_fast	94.34 7	96.74 137	96.51 149	97.44 142	97.69 245	94.15 155	96.02 158	98.43 173	93.17 232	97.30 166	97.38 198	95.48 125	99.28 249	93.74 203	99.34 163	98.88 193
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
RPSCF		97.87 63	97.51 85	98.95 17	99.15 73	98.43 3	97.56 76	99.06 41	96.19 112	98.48 65	98.70 58	94.72 148	99.24 255	94.37 177	99.33 170	99.17 133

LF4IMVS		96.07 169	95.63 184	97.36 149	98.19 177	95.55 92	95.44 190	98.82 114	92.29 251	95.70 252	96.55 252	92.63 203	98.69 315	91.75 239	99.33 170	97.85 284

9.14			96.69 134		98.53 142		96.02 158	98.98 66	93.23 226	97.18 172	97.46 187	96.47 88	99.62 148	92.99 219	99.32 172

tttt0517		93.31 272	92.56 279	95.57 239	98.71 119	87.86 283	97.44 85	87.17 365	95.79 137	97.47 160	96.84 234	64.12 364	99.81 32	96.20 77	99.32 172	99.02 167

Regformer-1		97.27 107	97.16 108	97.61 121	97.21 283	93.86 165	94.85 233	98.04 229	97.62 60	98.03 120	97.50 184	95.34 130	99.63 140	96.52 67	99.31 174	99.35 95

Regformer-2		97.41 98	97.24 103	97.93 97	97.21 283	94.72 131	94.85 233	98.27 194	97.74 51	98.11 108	97.50 184	95.58 121	99.69 116	96.57 66	99.31 174	99.37 92

N_pmnet		95.18 206	94.23 238	98.06 88	97.85 213	96.55 58	92.49 308	91.63 345	89.34 284	98.09 112	97.41 191	90.33 242	99.06 279	91.58 241	99.31 174	98.56 226

CDS-MVSNet		94.88 219	94.12 243	97.14 160	97.64 251	93.57 178	93.96 271	97.06 276	90.05 279	96.30 225	96.55 252	86.10 284	99.47 191	90.10 280	99.31 174	98.40 235
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
VPNet		97.26 108	97.49 88	96.59 191	99.47 28	90.58 238	96.27 142	98.53 163	97.77 47	98.46 68	98.41 78	94.59 155	99.68 123	94.61 165	99.29 178	99.52 42

114514_t		93.96 256	93.22 263	96.19 214	99.06 88	90.97 231	95.99 160	98.94 74	73.88 365	93.43 315	96.93 229	92.38 212	99.37 226	89.09 294	99.28 179	98.25 256

DELS-MVS		96.17 166	96.23 159	95.99 220	97.55 258	90.04 243	92.38 312	98.52 164	94.13 201	96.55 213	97.06 220	94.99 142	99.58 159	95.62 109	99.28 179	98.37 239
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
MVS_111021_HR		96.73 139	96.54 145	97.27 153	98.35 161	93.66 176	93.42 287	98.36 185	94.74 179	96.58 209	96.76 242	96.54 82	98.99 287	94.87 155	99.27 181	99.15 137

pmmvs5		94.63 234	94.34 236	95.50 243	97.63 252	88.34 273	94.02 266	97.13 272	87.15 307	95.22 261	97.15 212	87.50 276	99.27 251	93.99 195	99.26 182	98.88 193

DVP-MVS++.		97.96 46	97.90 45	98.12 83	97.75 239	95.40 101	99.03 7	98.89 79	96.62 92	98.62 52	98.30 90	96.97 56	99.75 65	95.70 101	99.25 183	99.21 126

PC_three_1452									87.24 305	98.37 75	97.44 189	97.00 54	96.78 361	92.01 229	99.25 183	99.21 126

OPU-MVS				97.64 119	98.01 198	95.27 111	96.79 118				97.35 201	96.97 56	98.51 332	91.21 249	99.25 183	99.14 140

APD-MVS_3200maxsize		98.13 37	97.90 45	98.79 31	98.79 108	97.31 37	97.55 77	98.92 76	97.72 54	98.25 93	98.13 113	97.10 45	99.75 65	95.44 121	99.24 186	99.32 98

PVSNet_Blended_VisFu		95.95 176	95.80 178	96.42 202	99.28 48	90.62 237	95.31 203	99.08 37	88.40 295	96.97 191	98.17 111	92.11 216	99.78 43	93.64 207	99.21 187	98.86 196

SR-MVS-dyc-post		98.14 34	97.84 51	99.02 9	98.81 105	98.05 9	97.55 77	98.86 90	97.77 47	98.20 97	98.07 121	96.60 80	99.76 58	95.49 114	99.20 188	99.26 117

RE-MVS-def			97.88 49		98.81 105	98.05 9	97.55 77	98.86 90	97.77 47	98.20 97	98.07 121	96.94 58		95.49 114	99.20 188	99.26 117

HQP_MVS		96.66 146	96.33 156	97.68 117	98.70 121	94.29 148	96.50 131	98.75 125	96.36 105	96.16 232	96.77 240	91.91 225	99.46 194	92.59 224	99.20 188	99.28 112

plane_prior5								98.75 125					99.46 194	92.59 224	99.20 188	99.28 112

CS-MVS-test		96.62 148	96.59 138	96.69 186	97.88 212	93.16 188	97.21 98	99.53 6	95.61 145	93.72 301	95.33 301	95.49 123	99.69 116	95.37 128	99.19 192	97.22 305

test1172		98.08 39	97.76 59	99.05 6	98.78 110	98.07 7	97.41 89	98.85 94	97.57 61	98.15 104	97.96 136	96.60 80	99.76 58	95.30 130	99.18 193	99.33 97

ppachtmachnet_test		94.49 240	94.84 211	93.46 302	96.16 315	82.10 343	90.59 339	97.48 262	90.53 274	97.01 187	97.59 176	91.01 233	99.36 228	93.97 197	99.18 193	98.94 176

HPM-MVS++		96.99 117	96.38 153	98.81 29	98.64 126	97.59 24	95.97 162	98.20 203	95.51 150	95.06 263	96.53 254	94.10 169	99.70 108	94.29 181	99.15 195	99.13 143

ETH3 D test6400		94.77 223	93.87 252	97.47 136	98.12 191	93.73 171	94.56 244	98.70 139	85.45 325	94.70 273	95.93 288	91.77 227	99.63 140	86.45 324	99.14 196	99.05 163

pmmvs4		94.82 221	94.19 241	96.70 185	97.42 268	92.75 198	92.09 317	96.76 286	86.80 311	95.73 251	97.22 210	89.28 260	98.89 297	93.28 213	99.14 196	98.46 234

TSAR-MVS + GP.		96.47 155	96.12 164	97.49 134	97.74 242	95.23 113	94.15 260	96.90 281	93.26 225	98.04 119	96.70 245	94.41 161	98.89 297	94.77 162	99.14 196	98.37 239

RRT_test8_iter05		92.46 284	92.52 280	92.29 327	95.33 336	77.43 359	95.73 174	98.55 162	94.41 190	97.46 161	97.72 167	57.44 369	99.74 75	96.92 59	99.14 196	99.69 20

CDPH-MVS		95.45 196	94.65 219	97.84 104	98.28 166	94.96 124	93.73 279	98.33 190	85.03 330	95.44 256	96.60 250	95.31 132	99.44 201	90.01 281	99.13 200	99.11 152

MVSFormer		96.14 167	96.36 154	95.49 244	97.68 246	87.81 286	98.67 13	99.02 52	96.50 100	94.48 280	96.15 274	86.90 280	99.92 4	98.73 7	99.13 200	98.74 209

lupinMVS		93.77 259	93.28 260	95.24 252	97.68 246	87.81 286	92.12 315	96.05 296	84.52 334	94.48 280	95.06 307	86.90 280	99.63 140	93.62 208	99.13 200	98.27 254

LFMVS		95.32 201	94.88 209	96.62 189	98.03 195	91.47 225	97.65 70	90.72 354	99.11 9	97.89 135	98.31 86	79.20 316	99.48 188	93.91 199	99.12 203	98.93 180

SR-MVS		98.00 45	97.66 67	99.01 11	98.77 112	97.93 11	97.38 90	98.83 106	97.32 75	98.06 116	97.85 151	96.65 75	99.77 53	95.00 152	99.11 204	99.32 98

thisisatest0530		92.71 281	91.76 289	95.56 241	98.42 156	88.23 274	96.03 157	87.35 364	94.04 204	96.56 211	95.47 299	64.03 365	99.77 53	94.78 161	99.11 204	98.68 217

TSAR-MVS + MP.		97.42 97	97.23 104	98.00 93	99.38 39	95.00 123	97.63 72	98.20 203	93.00 237	98.16 102	98.06 126	95.89 103	99.72 85	95.67 104	99.10 206	99.28 112
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
VDDNet		96.98 120	96.84 126	97.41 145	99.40 37	93.26 185	97.94 52	95.31 314	99.26 7	98.39 74	99.18 27	87.85 275	99.62 148	95.13 145	99.09 207	99.35 95

IterMVS-SCA-FT		95.86 180	96.19 161	94.85 269	97.68 246	85.53 316	92.42 310	97.63 256	96.99 82	98.36 78	98.54 70	87.94 270	99.75 65	97.07 56	99.08 208	99.27 116

CNVR-MVS		96.92 123	96.55 143	98.03 92	98.00 202	95.54 93	94.87 231	98.17 209	94.60 184	96.38 219	97.05 221	95.67 118	99.36 228	95.12 146	99.08 208	99.19 130

Anonymous202405211		96.34 159	95.98 172	97.43 143	98.25 171	93.85 166	96.74 121	94.41 321	97.72 54	98.37 75	98.03 129	87.15 279	99.53 175	94.06 190	99.07 210	98.92 184

CHOSEN 280x420		89.98 313	89.19 319	92.37 325	95.60 330	81.13 349	86.22 359	97.09 274	81.44 346	87.44 361	93.15 329	73.99 340	99.47 191	88.69 300	99.07 210	96.52 331

ab-mvs		96.59 149	96.59 138	96.60 190	98.64 126	92.21 207	98.35 28	97.67 248	94.45 189	96.99 188	98.79 51	94.96 143	99.49 185	90.39 276	99.07 210	98.08 265

LCM-MVSNet-Re		97.33 104	97.33 96	97.32 151	98.13 190	93.79 169	96.99 111	99.65 3	96.74 90	99.47 13	98.93 44	96.91 63	99.84 25	90.11 279	99.06 213	98.32 246

new-patchmatchnet		95.67 185	96.58 140	92.94 316	97.48 261	80.21 351	92.96 298	98.19 208	94.83 177	98.82 43	98.79 51	93.31 186	99.51 183	95.83 99	99.04 214	99.12 148

MSLP-MVS++		96.42 158	96.71 133	95.57 239	97.82 220	90.56 240	95.71 175	98.84 99	94.72 180	96.71 204	97.39 196	94.91 145	98.10 350	95.28 131	99.02 215	98.05 274

IterMVS		95.42 197	95.83 177	94.20 292	97.52 259	83.78 337	92.41 311	97.47 263	95.49 151	98.06 116	98.49 73	87.94 270	99.58 159	96.02 87	99.02 215	99.23 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PCF-MVS	89.43 18	92.12 292	90.64 306	96.57 194	97.80 225	93.48 181	89.88 349	98.45 170	74.46 364	96.04 236	95.68 292	90.71 238	99.31 240	73.73 362	99.01 217	96.91 315
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
LS3D		97.77 73	97.50 87	98.57 48	96.24 309	97.58 25	98.45 25	98.85 94	98.58 27	97.51 152	97.94 141	95.74 116	99.63 140	95.19 136	98.97 218	98.51 229

test_prior3		95.91 177	95.39 190	97.46 139	97.79 231	94.26 152	93.33 292	98.42 176	94.21 198	94.02 292	96.25 269	93.64 180	99.34 233	91.90 232	98.96 219	98.79 202

test_prior2							93.33 292		94.21 198	94.02 292	96.25 269	93.64 180		91.90 232	98.96 219

VNet		96.84 128	96.83 127	96.88 174	98.06 193	92.02 214	96.35 139	97.57 259	97.70 56	97.88 136	97.80 158	92.40 211	99.54 173	94.73 164	98.96 219	99.08 157

3Dnovator+	96.13 3	97.73 75	97.59 79	98.15 81	98.11 192	95.60 91	98.04 48	98.70 139	98.13 39	96.93 193	98.45 76	95.30 133	99.62 148	95.64 108	98.96 219	99.24 123

ETH3D cwj APD-0.16		96.23 163	95.61 186	98.09 85	97.91 208	95.65 90	94.94 228	98.74 127	91.31 266	96.02 237	97.08 218	94.05 171	99.69 116	91.51 242	98.94 223	98.93 180

QAPM		95.88 179	95.57 187	96.80 179	97.90 210	91.84 219	98.18 42	98.73 129	88.41 294	96.42 217	98.13 113	94.73 147	99.75 65	88.72 299	98.94 223	98.81 200

ZD-MVS					98.43 155	95.94 78		98.56 161	90.72 272	96.66 206	97.07 219	95.02 141	99.74 75	91.08 250	98.93 225

plane_prior						94.29 148	95.42 192		94.31 195						98.93 225

train_agg		95.46 195	94.66 218	97.88 101	97.84 217	95.23 113	93.62 281	98.39 181	87.04 308	93.78 297	95.99 281	94.58 156	99.52 179	91.76 238	98.90 227	98.89 189

agg_prior2														90.34 278	98.90 227	99.10 156

ITE_SJBPF				97.85 103	98.64 126	96.66 54		98.51 166	95.63 143	97.22 168	97.30 206	95.52 122	98.55 329	90.97 253	98.90 227	98.34 245

test9_res														91.29 245	98.89 230	99.00 168

EPNet_dtu		91.39 301	90.75 304	93.31 304	90.48 370	82.61 340	94.80 235	92.88 334	93.39 220	81.74 368	94.90 312	81.36 307	99.11 273	88.28 306	98.87 231	98.21 259
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TAPA-MVS	93.32 12	94.93 216	94.23 238	97.04 166	98.18 180	94.51 140	95.22 211	98.73 129	81.22 347	96.25 228	95.95 286	93.80 177	98.98 289	89.89 283	98.87 231	97.62 294
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
agg_prior1		95.39 198	94.60 224	97.75 109	97.80 225	94.96 124	93.39 289	98.36 185	87.20 306	93.49 311	95.97 284	94.65 153	99.53 175	91.69 240	98.86 233	98.77 207

DP-MVS Recon		95.55 189	95.13 196	96.80 179	98.51 144	93.99 161	94.60 242	98.69 142	90.20 277	95.78 248	96.21 272	92.73 199	98.98 289	90.58 270	98.86 233	97.42 301

EIA-MVS		96.04 171	95.77 180	96.85 176	97.80 225	92.98 192	96.12 152	99.16 20	94.65 182	93.77 299	91.69 352	95.68 117	99.67 128	94.18 185	98.85 235	97.91 282

MCST-MVS		96.24 162	95.80 178	97.56 123	98.75 113	94.13 156	94.66 240	98.17 209	90.17 278	96.21 230	96.10 279	95.14 136	99.43 203	94.13 188	98.85 235	99.13 143

ETV-MVS		96.13 168	95.90 176	96.82 178	97.76 237	93.89 163	95.40 195	98.95 73	95.87 132	95.58 255	91.00 358	96.36 95	99.72 85	93.36 210	98.83 237	96.85 318

eth_miper_zixun_eth		94.89 218	94.93 206	94.75 274	95.99 320	86.12 311	91.35 326	98.49 167	93.40 219	97.12 176	97.25 209	86.87 282	99.35 231	95.08 148	98.82 238	98.78 204

testtj		96.69 143	96.13 163	98.36 61	98.46 154	96.02 76	96.44 133	98.70 139	94.26 196	96.79 198	97.13 213	94.07 170	99.75 65	90.53 271	98.80 239	99.31 103

HyFIR lowres test		93.72 261	92.65 276	96.91 173	98.93 98	91.81 220	91.23 331	98.52 164	82.69 340	96.46 216	96.52 256	80.38 312	99.90 13	90.36 277	98.79 240	99.03 165

test12				97.46 139	97.61 253	94.07 157		97.78 242		93.57 309		93.31 186	99.42 204		98.78 241	98.89 189

CMPMVS	73.10 23	92.74 280	91.39 292	96.77 181	93.57 357	94.67 136	94.21 257	97.67 248	80.36 351	93.61 307	96.60 250	82.85 302	97.35 356	84.86 338	98.78 241	98.29 253
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CNLPA		95.04 212	94.47 231	96.75 182	97.81 221	95.25 112	94.12 264	97.89 234	94.41 190	94.57 275	95.69 291	90.30 245	98.35 342	86.72 323	98.76 243	96.64 327

OpenMVS	94.22 8	95.48 193	95.20 193	96.32 207	97.16 286	91.96 216	97.74 66	98.84 99	87.26 304	94.36 282	98.01 132	93.95 173	99.67 128	90.70 266	98.75 244	97.35 304

testgi		96.07 169	96.50 150	94.80 272	99.26 49	87.69 289	95.96 163	98.58 160	95.08 167	98.02 122	96.25 269	97.92 16	97.60 355	88.68 301	98.74 245	99.11 152

HQP3-MVS								98.43 173							98.74 245

HQP-MVS		95.17 208	94.58 227	96.92 171	97.85 213	92.47 201	94.26 250	98.43 173	93.18 229	92.86 324	95.08 305	90.33 242	99.23 257	90.51 273	98.74 245	99.05 163

alignmvs		96.01 173	95.52 188	97.50 131	97.77 236	94.71 132	96.07 154	96.84 282	97.48 67	96.78 202	94.28 324	85.50 288	99.40 215	96.22 76	98.73 248	98.40 235

旧先验1					97.80 225	93.87 164		97.75 243			97.04 222	93.57 182			98.68 249	98.72 212

thisisatest0515		90.43 308	89.18 320	94.17 294	97.07 289	85.44 317	89.75 350	87.58 363	88.28 297	93.69 304	91.72 351	65.27 363	99.58 159	90.59 269	98.67 250	97.50 299

diffmvs		96.04 171	96.23 159	95.46 246	97.35 271	88.03 281	93.42 287	99.08 37	94.09 203	96.66 206	96.93 229	93.85 175	99.29 247	96.01 89	98.67 250	99.06 161

CL-MVSNet_self_test		95.04 212	94.79 215	95.82 230	97.51 260	89.79 248	91.14 333	96.82 284	93.05 235	96.72 203	96.40 262	90.82 236	99.16 266	91.95 231	98.66 252	98.50 230

test222					98.17 182	93.24 186	92.74 304	97.61 258	75.17 363	94.65 274	96.69 246	90.96 235			98.66 252	97.66 293

新几何1				97.25 156	98.29 164	94.70 135		97.73 244	77.98 358	94.83 270	96.67 247	92.08 218	99.45 198	88.17 308	98.65 254	97.61 295

1121		94.26 244	93.26 261	97.27 153	98.26 170	94.73 130	95.86 168	97.71 246	77.96 359	94.53 277	96.71 244	91.93 223	99.40 215	87.71 310	98.64 255	97.69 292

原ACMM1				96.58 192	98.16 184	92.12 211		98.15 214	85.90 318	93.49 311	96.43 259	92.47 210	99.38 223	87.66 313	98.62 256	98.23 257

PVSNet_Blended		93.96 256	93.65 255	94.91 264	97.79 231	87.40 294	91.43 324	98.68 144	84.50 335	94.51 278	94.48 320	93.04 191	99.30 243	89.77 285	98.61 257	98.02 277

AdaColmap		95.11 209	94.62 223	96.58 192	97.33 277	94.45 143	94.92 229	98.08 222	93.15 233	93.98 295	95.53 298	94.34 163	99.10 275	85.69 329	98.61 257	96.20 335

DSMNet-mixed		92.19 290	91.83 287	93.25 306	96.18 314	83.68 338	96.27 142	93.68 326	76.97 362	92.54 333	99.18 27	89.20 262	98.55 329	83.88 343	98.60 259	97.51 298

MSP-MVS		97.45 95	96.92 123	99.03 8	99.26 49	97.70 19	97.66 69	98.89 79	95.65 142	98.51 62	96.46 258	92.15 214	99.81 32	95.14 143	98.58 260	99.58 28
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
testdata				95.70 236	98.16 184	90.58 238		97.72 245	80.38 350	95.62 253	97.02 223	92.06 219	98.98 289	89.06 296	98.52 261	97.54 297

API-MVS		95.09 211	95.01 203	95.31 250	96.61 300	94.02 159	96.83 116	97.18 270	95.60 146	95.79 246	94.33 322	94.54 158	98.37 341	85.70 328	98.52 261	93.52 355

Effi-MVS+-dtu		96.81 133	96.09 166	98.99 13	96.90 296	98.69 2	96.42 134	98.09 220	95.86 133	95.15 262	95.54 297	94.26 165	99.81 32	94.06 190	98.51 263	98.47 232

canonicalmvs		97.23 111	97.21 106	97.30 152	97.65 250	94.39 144	97.84 59	99.05 43	97.42 69	96.68 205	93.85 327	97.63 26	99.33 236	96.29 75	98.47 264	98.18 262

NCCC		96.52 152	95.99 171	98.10 84	97.81 221	95.68 87	95.00 226	98.20 203	95.39 155	95.40 258	96.36 265	93.81 176	99.45 198	93.55 209	98.42 265	99.17 133

Patchmatch-test		93.60 266	93.25 262	94.63 277	96.14 318	87.47 292	96.04 156	94.50 320	93.57 215	96.47 215	96.97 226	76.50 331	98.61 323	90.67 267	98.41 266	97.81 288

cl22		93.25 274	92.84 270	94.46 285	94.30 347	86.00 312	91.09 335	96.64 291	90.74 271	95.79 246	96.31 267	78.24 320	98.77 307	94.15 187	98.34 267	98.62 221

miper_ehance_all_eth		94.69 229	94.70 217	94.64 276	95.77 326	86.22 310	91.32 329	98.24 198	91.67 259	97.05 183	96.65 248	88.39 267	99.22 259	94.88 154	98.34 267	98.49 231

miper_enhance_ethall		93.14 276	92.78 273	94.20 292	93.65 355	85.29 320	89.97 345	97.85 236	85.05 329	96.15 234	94.56 316	85.74 286	99.14 268	93.74 203	98.34 267	98.17 263

CVMVSNet		92.33 288	92.79 271	90.95 334	97.26 280	75.84 364	95.29 205	92.33 340	81.86 342	96.27 226	98.19 108	81.44 306	98.46 334	94.23 184	98.29 270	98.55 228

our_test_3		94.20 250	94.58 227	93.07 310	96.16 315	81.20 348	90.42 341	96.84 282	90.72 272	97.14 174	97.13 213	90.47 240	99.11 273	94.04 194	98.25 271	98.91 185

xiu_mvs_v1_base_debu		95.62 186	95.96 173	94.60 279	98.01 198	88.42 270	93.99 268	98.21 200	92.98 238	95.91 241	94.53 317	96.39 92	99.72 85	95.43 124	98.19 272	95.64 342

xiu_mvs_v1_base		95.62 186	95.96 173	94.60 279	98.01 198	88.42 270	93.99 268	98.21 200	92.98 238	95.91 241	94.53 317	96.39 92	99.72 85	95.43 124	98.19 272	95.64 342

xiu_mvs_v1_base_debi		95.62 186	95.96 173	94.60 279	98.01 198	88.42 270	93.99 268	98.21 200	92.98 238	95.91 241	94.53 317	96.39 92	99.72 85	95.43 124	98.19 272	95.64 342

XVG-OURS		97.12 113	96.74 132	98.26 69	98.99 95	97.45 33	93.82 275	99.05 43	95.19 162	98.32 86	97.70 168	95.22 135	98.41 336	94.27 182	98.13 275	98.93 180

sss		94.22 246	93.72 254	95.74 233	97.71 244	89.95 245	93.84 274	96.98 278	88.38 296	93.75 300	95.74 290	87.94 270	98.89 297	91.02 252	98.10 276	98.37 239

DPM-MVS		93.68 263	92.77 274	96.42 202	97.91 208	92.54 199	91.17 332	97.47 263	84.99 331	93.08 321	94.74 313	89.90 250	99.00 285	87.54 316	98.09 277	97.72 290

MIMVSNet		93.42 269	92.86 268	95.10 258	98.17 182	88.19 275	98.13 44	93.69 324	92.07 252	95.04 266	98.21 107	80.95 310	99.03 284	81.42 350	98.06 278	98.07 267

pmmvs3		90.00 312	88.90 321	93.32 303	94.20 351	85.34 318	91.25 330	92.56 339	78.59 356	93.82 296	95.17 303	67.36 362	98.69 315	89.08 295	98.03 279	95.92 337

Fast-Effi-MVS+-dtu		96.44 156	96.12 164	97.39 147	97.18 285	94.39 144	95.46 189	98.73 129	96.03 122	94.72 271	94.92 311	96.28 98	99.69 116	93.81 201	97.98 280	98.09 264

thres600view7		92.03 293	91.43 291	93.82 295	98.19 177	84.61 330	96.27 142	90.39 355	96.81 88	96.37 220	93.11 330	73.44 348	99.49 185	80.32 352	97.95 281	97.36 302

MS-PatchMatch		94.83 220	94.91 208	94.57 282	96.81 298	87.10 299	94.23 255	97.34 265	88.74 292	97.14 174	97.11 216	91.94 222	98.23 346	92.99 219	97.92 282	98.37 239

1112_ss		94.12 251	93.42 258	96.23 211	98.59 136	90.85 232	94.24 254	98.85 94	85.49 322	92.97 322	94.94 309	86.01 285	99.64 138	91.78 237	97.92 282	98.20 260

MVS_Test		96.27 161	96.79 131	94.73 275	96.94 294	86.63 305	96.18 149	98.33 190	94.94 173	96.07 235	98.28 95	95.25 134	99.26 252	97.21 48	97.90 284	98.30 250

Fast-Effi-MVS+		95.49 191	95.07 199	96.75 182	97.67 249	92.82 195	94.22 256	98.60 157	91.61 260	93.42 316	92.90 337	96.73 73	99.70 108	92.60 222	97.89 285	97.74 289

test_yl		94.40 241	94.00 247	95.59 237	96.95 292	89.52 252	94.75 238	95.55 310	96.18 113	96.79 198	96.14 276	81.09 308	99.18 261	90.75 261	97.77 286	98.07 267

DCV-MVSNet		94.40 241	94.00 247	95.59 237	96.95 292	89.52 252	94.75 238	95.55 310	96.18 113	96.79 198	96.14 276	81.09 308	99.18 261	90.75 261	97.77 286	98.07 267

Test_1112_low_res		93.53 268	92.86 268	95.54 242	98.60 134	88.86 264	92.75 302	98.69 142	82.66 341	92.65 329	96.92 231	84.75 293	99.56 166	90.94 254	97.76 288	98.19 261

thres100view900		91.76 297	91.26 296	93.26 305	98.21 175	84.50 331	96.39 135	90.39 355	96.87 86	96.33 221	93.08 334	73.44 348	99.42 204	78.85 356	97.74 289	95.85 338

tfpn200view9		91.55 299	91.00 298	93.21 308	98.02 196	84.35 333	95.70 176	90.79 352	96.26 109	95.90 244	92.13 347	73.62 345	99.42 204	78.85 356	97.74 289	95.85 338

thres400		91.68 298	91.00 298	93.71 297	98.02 196	84.35 333	95.70 176	90.79 352	96.26 109	95.90 244	92.13 347	73.62 345	99.42 204	78.85 356	97.74 289	97.36 302

BH-RMVSNet		94.56 237	94.44 234	94.91 264	97.57 254	87.44 293	93.78 278	96.26 293	93.69 214	96.41 218	96.50 257	92.10 217	99.00 285	85.96 326	97.71 292	98.31 248

MG-MVS		94.08 254	94.00 247	94.32 289	97.09 288	85.89 313	93.19 296	95.96 300	92.52 246	94.93 269	97.51 183	89.54 254	98.77 307	87.52 317	97.71 292	98.31 248

PVSNet	86.72 19	91.10 303	90.97 300	91.49 330	97.56 256	78.04 356	87.17 357	94.60 319	84.65 333	92.34 334	92.20 346	87.37 278	98.47 333	85.17 336	97.69 294	97.96 279

PatchMatch-RL		94.61 235	93.81 253	97.02 168	98.19 177	95.72 83	93.66 280	97.23 267	88.17 298	94.94 268	95.62 295	91.43 229	98.57 326	87.36 319	97.68 295	96.76 324

OpenMVS_ROB	91.80 14	93.64 265	93.05 264	95.42 247	97.31 279	91.21 227	95.08 219	96.68 290	81.56 344	96.88 197	96.41 260	90.44 241	99.25 254	85.39 333	97.67 296	95.80 340

SCA		93.38 271	93.52 257	92.96 315	96.24 309	81.40 347	93.24 294	94.00 323	91.58 262	94.57 275	96.97 226	87.94 270	99.42 204	89.47 289	97.66 297	98.06 271

MSDG		95.33 200	95.13 196	95.94 226	97.40 269	91.85 218	91.02 336	98.37 184	95.30 158	96.31 224	95.99 281	94.51 159	98.38 339	89.59 287	97.65 298	97.60 296

thres200		91.00 305	90.42 309	92.77 318	97.47 265	83.98 336	94.01 267	91.18 350	95.12 166	95.44 256	91.21 356	73.93 341	99.31 240	77.76 359	97.63 299	95.01 348

new_pmnet		92.34 287	91.69 290	94.32 289	96.23 311	89.16 259	92.27 313	92.88 334	84.39 337	95.29 259	96.35 266	85.66 287	96.74 362	84.53 340	97.56 300	97.05 309

Effi-MVS+		96.19 165	96.01 169	96.71 184	97.43 267	92.19 210	96.12 152	99.10 31	95.45 152	93.33 318	94.71 314	97.23 43	99.56 166	93.21 216	97.54 301	98.37 239

F-COLMAP		95.30 202	94.38 235	98.05 91	98.64 126	96.04 74	95.61 186	98.66 149	89.00 288	93.22 319	96.40 262	92.90 195	99.35 231	87.45 318	97.53 302	98.77 207

MAR-MVS		94.21 248	93.03 265	97.76 108	96.94 294	97.44 34	96.97 112	97.15 271	87.89 302	92.00 337	92.73 341	92.14 215	99.12 270	83.92 342	97.51 303	96.73 325
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
xiu_mvs_v2_base		94.22 246	94.63 222	92.99 314	97.32 278	84.84 328	92.12 315	97.84 238	91.96 255	94.17 286	93.43 328	96.07 100	99.71 99	91.27 246	97.48 304	94.42 351

PS-MVSNAJ		94.10 252	94.47 231	93.00 313	97.35 271	84.88 327	91.86 319	97.84 238	91.96 255	94.17 286	92.50 344	95.82 108	99.71 99	91.27 246	97.48 304	94.40 352

cascas		91.89 295	91.35 293	93.51 301	94.27 348	85.60 315	88.86 354	98.61 156	79.32 354	92.16 336	91.44 354	89.22 261	98.12 349	90.80 259	97.47 306	96.82 321

test-LLR		89.97 314	89.90 312	90.16 338	94.24 349	74.98 365	89.89 346	89.06 360	92.02 253	89.97 350	90.77 359	73.92 342	98.57 326	91.88 234	97.36 307	96.92 313

test-mter		87.92 329	87.17 330	90.16 338	94.24 349	74.98 365	89.89 346	89.06 360	86.44 313	89.97 350	90.77 359	54.96 375	98.57 326	91.88 234	97.36 307	96.92 313

GA-MVS		92.83 279	92.15 284	94.87 268	96.97 291	87.27 297	90.03 344	96.12 295	91.83 258	94.05 291	94.57 315	76.01 335	98.97 293	92.46 226	97.34 309	98.36 244

MVP-Stereo		95.69 183	95.28 192	96.92 171	98.15 186	93.03 191	95.64 185	98.20 203	90.39 275	96.63 208	97.73 165	91.63 228	99.10 275	91.84 236	97.31 310	98.63 220
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
mvs_anonymous		95.36 199	96.07 168	93.21 308	96.29 307	81.56 346	94.60 242	97.66 250	93.30 224	96.95 192	98.91 46	93.03 193	99.38 223	96.60 63	97.30 311	98.69 215

mvs-test1		96.20 164	95.50 189	98.32 64	96.90 296	98.16 5	95.07 220	98.09 220	95.86 133	93.63 305	94.32 323	94.26 165	99.71 99	94.06 190	97.27 312	97.07 308

AUN-MVS		93.95 258	92.69 275	97.74 110	97.80 225	95.38 103	95.57 187	95.46 312	91.26 267	92.64 330	96.10 279	74.67 339	99.55 170	93.72 205	96.97 313	98.30 250

hse-mvs2		95.77 182	95.09 198	97.79 106	97.84 217	95.51 95	95.66 180	95.43 313	96.58 96	97.21 170	96.16 273	84.14 296	99.54 173	95.89 95	96.92 314	98.32 246

TESTMET0.1,1		87.20 332	86.57 334	89.07 342	93.62 356	72.84 369	89.89 346	87.01 366	85.46 324	89.12 355	90.20 361	56.00 374	97.72 354	90.91 255	96.92 314	96.64 327

EMVS		89.06 320	89.22 316	88.61 344	93.00 362	77.34 360	82.91 363	90.92 351	94.64 183	92.63 331	91.81 350	76.30 333	97.02 357	83.83 344	96.90 316	91.48 362

YYNet1		94.73 224	94.84 211	94.41 287	97.47 265	85.09 325	90.29 342	95.85 303	92.52 246	97.53 150	97.76 159	91.97 220	99.18 261	93.31 212	96.86 317	98.95 174

WTY-MVS		93.55 267	93.00 266	95.19 255	97.81 221	87.86 283	93.89 273	96.00 298	89.02 287	94.07 290	95.44 300	86.27 283	99.33 236	87.69 312	96.82 318	98.39 237

E-PMN		89.52 318	89.78 313	88.73 343	93.14 360	77.61 358	83.26 362	92.02 341	94.82 178	93.71 302	93.11 330	75.31 337	96.81 359	85.81 327	96.81 319	91.77 361

MDA-MVSNet_test_wron		94.73 224	94.83 213	94.42 286	97.48 261	85.15 323	90.28 343	95.87 302	92.52 246	97.48 158	97.76 159	91.92 224	99.17 265	93.32 211	96.80 320	98.94 176

BH-untuned		94.69 229	94.75 216	94.52 284	97.95 207	87.53 291	94.07 265	97.01 277	93.99 205	97.10 178	95.65 293	92.65 202	98.95 294	87.60 314	96.74 321	97.09 307

PLC	91.02 16	94.05 255	92.90 267	97.51 128	98.00 202	95.12 121	94.25 253	98.25 197	86.17 314	91.48 340	95.25 302	91.01 233	99.19 260	85.02 337	96.69 322	98.22 258
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PMMVS		92.39 285	91.08 297	96.30 209	93.12 361	92.81 196	90.58 340	95.96 300	79.17 355	91.85 339	92.27 345	90.29 246	98.66 320	89.85 284	96.68 323	97.43 300

ET-MVSNet_ETH3D		91.12 302	89.67 314	95.47 245	96.41 305	89.15 260	91.54 323	90.23 358	89.07 286	86.78 364	92.84 338	69.39 359	99.44 201	94.16 186	96.61 324	97.82 286

MVS-HIRNet		88.40 325	90.20 311	82.99 349	97.01 290	60.04 373	93.11 297	85.61 368	84.45 336	88.72 356	99.09 33	84.72 294	98.23 346	82.52 348	96.59 325	90.69 364

MDTV_nov1_ep13			91.28 294		94.31 346	73.51 368	94.80 235	93.16 331	86.75 312	93.45 314	97.40 192	76.37 332	98.55 329	88.85 297	96.43 326

XVG-OURS-SEG-HR		97.38 100	97.07 114	98.30 67	99.01 94	97.41 35	94.66 240	99.02 52	95.20 161	98.15 104	97.52 182	98.83 4	98.43 335	94.87 155	96.41 327	99.07 159

MDA-MVSNet-bldmvs		95.69 183	95.67 182	95.74 233	98.48 150	88.76 268	92.84 299	97.25 266	96.00 123	97.59 148	97.95 140	91.38 230	99.46 194	93.16 217	96.35 328	98.99 171

PAPM_NR		94.61 235	94.17 242	95.96 222	98.36 160	91.23 226	95.93 166	97.95 230	92.98 238	93.42 316	94.43 321	90.53 239	98.38 339	87.60 314	96.29 329	98.27 254

UnsupCasMVSNet_bld		94.72 228	94.26 237	96.08 218	98.62 131	90.54 241	93.38 290	98.05 228	90.30 276	97.02 186	96.80 239	89.54 254	99.16 266	88.44 303	96.18 330	98.56 226

h-mvs33		96.29 160	95.63 184	98.26 69	98.50 147	96.11 72	96.90 113	97.09 274	96.58 96	97.21 170	98.19 108	84.14 296	99.78 43	95.89 95	96.17 331	98.89 189

FPMVS		89.92 315	88.63 322	93.82 295	98.37 159	96.94 46	91.58 322	93.34 330	88.00 300	90.32 347	97.10 217	70.87 356	91.13 367	71.91 365	96.16 332	93.39 357

CR-MVSNet		93.29 273	92.79 271	94.78 273	95.44 333	88.15 277	96.18 149	97.20 268	84.94 332	94.10 288	98.57 66	77.67 323	99.39 220	95.17 138	95.81 333	96.81 322

PatchT		93.75 260	93.57 256	94.29 291	95.05 339	87.32 296	96.05 155	92.98 333	97.54 65	94.25 284	98.72 56	75.79 336	99.24 255	95.92 93	95.81 333	96.32 333

RPMNet		94.68 231	94.60 224	94.90 266	95.44 333	88.15 277	96.18 149	98.86 90	97.43 68	94.10 288	98.49 73	79.40 314	99.76 58	95.69 103	95.81 333	96.81 322

HY-MVS	91.43 15	92.58 282	91.81 288	94.90 266	96.49 303	88.87 263	97.31 91	94.62 318	85.92 317	90.50 346	96.84 234	85.05 290	99.40 215	83.77 345	95.78 336	96.43 332

PAPR		92.22 289	91.27 295	95.07 259	95.73 328	88.81 265	91.97 318	97.87 235	85.80 319	90.91 342	92.73 341	91.16 231	98.33 343	79.48 353	95.76 337	98.08 265

gg-mvs-nofinetune		88.28 326	86.96 331	92.23 328	92.84 364	84.44 332	98.19 41	74.60 372	99.08 10	87.01 363	99.47 8	56.93 370	98.23 346	78.91 355	95.61 338	94.01 353

MVS		90.02 311	89.20 318	92.47 323	94.71 342	86.90 302	95.86 168	96.74 288	64.72 367	90.62 343	92.77 339	92.54 207	98.39 338	79.30 354	95.56 339	92.12 359

1314		92.38 286	92.30 282	92.64 320	95.42 335	85.15 323	95.86 168	96.97 279	85.40 326	90.62 343	93.06 335	91.12 232	97.80 353	86.74 322	95.49 340	94.97 349

KD-MVS_2432*1600		88.93 321	87.74 326	92.49 321	88.04 371	81.99 344	89.63 351	95.62 306	91.35 264	95.06 263	93.11 330	56.58 371	98.63 321	85.19 334	95.07 341	96.85 318

miper_refine_blended		88.93 321	87.74 326	92.49 321	88.04 371	81.99 344	89.63 351	95.62 306	91.35 264	95.06 263	93.11 330	56.58 371	98.63 321	85.19 334	95.07 341	96.85 318

TR-MVS		92.54 283	92.20 283	93.57 300	96.49 303	86.66 304	93.51 285	94.73 317	89.96 280	94.95 267	93.87 326	90.24 247	98.61 323	81.18 351	94.88 343	95.45 346

MVE	73.61 22	86.48 333	85.92 335	88.18 346	96.23 311	85.28 321	81.78 364	75.79 371	86.01 315	82.53 367	91.88 349	92.74 198	87.47 369	71.42 366	94.86 344	91.78 360
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
BH-w/o		92.14 291	91.94 285	92.73 319	97.13 287	85.30 319	92.46 309	95.64 305	89.33 285	94.21 285	92.74 340	89.60 252	98.24 345	81.68 349	94.66 345	94.66 350

UnsupCasMVSNet_eth		95.91 177	95.73 181	96.44 200	98.48 150	91.52 224	95.31 203	98.45 170	95.76 138	97.48 158	97.54 179	89.53 256	98.69 315	94.43 173	94.61 346	99.13 143

baseline2		89.65 317	88.44 324	93.25 306	95.62 329	82.71 339	93.82 275	85.94 367	88.89 290	87.35 362	92.54 343	71.23 354	99.33 236	86.01 325	94.60 347	97.72 290

PatchmatchNet		91.98 294	91.87 286	92.30 326	94.60 344	79.71 352	95.12 214	93.59 328	89.52 283	93.61 307	97.02 223	77.94 321	99.18 261	90.84 257	94.57 348	98.01 278
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm		91.08 304	90.85 302	91.75 329	95.33 336	78.09 355	95.03 225	91.27 349	88.75 291	93.53 310	97.40 192	71.24 353	99.30 243	91.25 248	93.87 349	97.87 283

IB-MVS	85.98 20	88.63 323	86.95 332	93.68 298	95.12 338	84.82 329	90.85 337	90.17 359	87.55 303	88.48 357	91.34 355	58.01 368	99.59 157	87.24 320	93.80 350	96.63 329
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		90.11 310	89.21 317	92.83 317	93.89 353	86.87 303	91.74 321	88.74 362	92.02 253	94.71 272	91.14 357	73.92 342	94.48 365	83.75 346	92.94 351	97.16 306

PAPM		87.64 331	85.84 336	93.04 311	96.54 301	84.99 326	88.42 356	95.57 309	79.52 353	83.82 365	93.05 336	80.57 311	98.41 336	62.29 368	92.79 352	95.71 341

CostFormer		89.75 316	89.25 315	91.26 333	94.69 343	78.00 357	95.32 202	91.98 342	81.50 345	90.55 345	96.96 228	71.06 355	98.89 297	88.59 302	92.63 353	96.87 316

tpm2		88.47 324	87.69 328	90.79 335	94.98 340	77.34 360	95.09 217	91.83 343	77.51 361	89.40 352	96.41 260	67.83 361	98.73 311	83.58 347	92.60 354	96.29 334

GG-mvs-BLEND				90.60 336	91.00 368	84.21 335	98.23 35	72.63 375		82.76 366	84.11 366	56.14 373	96.79 360	72.20 364	92.09 355	90.78 363

ADS-MVSNet2		91.47 300	90.51 308	94.36 288	95.51 331	85.63 314	95.05 223	95.70 304	83.46 338	92.69 327	96.84 234	79.15 317	99.41 213	85.66 330	90.52 356	98.04 275

ADS-MVSNet		90.95 306	90.26 310	93.04 311	95.51 331	82.37 342	95.05 223	93.41 329	83.46 338	92.69 327	96.84 234	79.15 317	98.70 314	85.66 330	90.52 356	98.04 275

JIA-IIPM		91.79 296	90.69 305	95.11 257	93.80 354	90.98 230	94.16 259	91.78 344	96.38 104	90.30 348	99.30 18	72.02 352	98.90 295	88.28 306	90.17 358	95.45 346

tpmvs		90.79 307	90.87 301	90.57 337	92.75 365	76.30 362	95.79 173	93.64 327	91.04 270	91.91 338	96.26 268	77.19 329	98.86 301	89.38 291	89.85 359	96.56 330

EPMVS		89.26 319	88.55 323	91.39 331	92.36 366	79.11 353	95.65 183	79.86 370	88.60 293	93.12 320	96.53 254	70.73 357	98.10 350	90.75 261	89.32 360	96.98 311

baseline1		93.14 276	92.64 277	94.62 278	97.34 275	87.20 298	96.67 128	93.02 332	94.71 181	96.51 214	95.83 289	81.64 305	98.60 325	90.00 282	88.06 361	98.07 267

DWT-MVSNet_test		87.92 329	86.77 333	91.39 331	93.18 358	78.62 354	95.10 215	91.42 346	85.58 321	88.00 358	88.73 363	60.60 367	98.90 295	90.60 268	87.70 362	96.65 326

tpmrst		90.31 309	90.61 307	89.41 341	94.06 352	72.37 370	95.06 222	93.69 324	88.01 299	92.32 335	96.86 232	77.45 325	98.82 302	91.04 251	87.01 363	97.04 310

tpm cat1		88.01 328	87.33 329	90.05 340	94.48 345	76.28 363	94.47 247	94.35 322	73.84 366	89.26 353	95.61 296	73.64 344	98.30 344	84.13 341	86.20 364	95.57 345

DeepMVS_CX				77.17 350	90.94 369	85.28 321		74.08 374	52.51 368	80.87 369	88.03 364	75.25 338	70.63 370	59.23 369	84.94 365	75.62 365

dp		88.08 327	88.05 325	88.16 347	92.85 363	68.81 372	94.17 258	92.88 334	85.47 323	91.38 341	96.14 276	68.87 360	98.81 304	86.88 321	83.80 366	96.87 316

tmp_tt		57.23 336	62.50 339	41.44 352	34.77 375	49.21 375	83.93 360	60.22 376	15.31 369	71.11 370	79.37 367	70.09 358	44.86 371	64.76 367	82.93 367	30.25 367

test_method		66.88 335	66.13 338	69.11 351	62.68 374	25.73 376	49.76 365	96.04 297	14.32 370	64.27 371	91.69 352	73.45 347	88.05 368	76.06 361	66.94 368	93.54 354

PVSNet_0	81.89 21	84.49 334	83.21 337	88.34 345	95.76 327	74.97 367	83.49 361	92.70 338	78.47 357	87.94 359	86.90 365	83.38 301	96.63 363	73.44 363	66.86 369	93.40 356

test123		12.59 338	15.49 341	3.87 353	6.07 376	2.55 377	90.75 338	2.59 378	2.52 371	5.20 373	13.02 370	4.96 376	1.85 373	5.20 370	9.09 370	7.23 368

testmvs		12.33 339	15.23 342	3.64 354	5.77 377	2.23 378	88.99 353	3.62 377	2.30 372	5.29 372	13.09 369	4.52 377	1.95 372	5.16 371	8.32 371	6.75 369

test_blank		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uanet_test		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

cdsmvs_eth3d_5k		24.22 337	32.30 340	0.00 355	0.00 378	0.00 379	0.00 366	98.10 219	0.00 373	0.00 374	95.06 307	97.54 29	0.00 374	0.00 372	0.00 372	0.00 370

pcd_1.5k_mvsjas		7.98 340	10.65 343	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	95.82 108	0.00 374	0.00 372	0.00 372	0.00 370

sosnet-low-res		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

sosnet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uncertanet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

Regformer		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

ab-mvs-re		7.91 341	10.55 344	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	94.94 309	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uanet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

FOURS1					99.59 14	98.20 4	99.03 7	99.25 12	98.96 18	98.87 40

test_one_0601					99.05 91	95.50 98		98.87 87	97.21 79	98.03 120	98.30 90	96.93 60

eth-test2					0.00 378

eth-test					0.00 378

test_241102_ONE					99.22 58	95.35 106		98.83 106	96.04 120	99.08 31	98.13 113	97.87 20	99.33 236

save fliter					98.48 150	94.71 132	94.53 245	98.41 178	95.02 171

test0726					99.24 53	95.51 95	96.89 114	98.89 79	95.92 128	98.64 51	98.31 86	97.06 50

GSMVS																98.06 271

test_part2					99.03 93	96.07 73				98.08 114

sam_mvs1												77.80 322				98.06 271

sam_mvs												77.38 326

MTGPA								98.73 129

test_post1							94.98 227				10.37 372	76.21 334	99.04 281	89.47 289

test_post											10.87 371	76.83 330	99.07 278

patchmatchnet-post											96.84 234	77.36 327	99.42 204

MTMP							96.55 129	74.60 372

gm-plane-assit					91.79 367	71.40 371			81.67 343		90.11 362		98.99 287	84.86 338

TEST9					97.84 217	95.23 113	93.62 281	98.39 181	86.81 310	93.78 297	95.99 281	94.68 151	99.52 179

test_8					97.81 221	95.07 122	93.54 284	98.38 183	87.04 308	93.71 302	95.96 285	94.58 156	99.52 179

agg_prior					97.80 225	94.96 124		98.36 185		93.49 311			99.53 175

test_prior4						95.38 103	93.61 283

test_prior				97.46 139	97.79 231	94.26 152		98.42 176					99.34 233			98.79 202

旧先验2							93.35 291		77.95 360	95.77 250			98.67 319	90.74 264

新几何2							93.43 286

无先验							93.20 295	97.91 232	80.78 348				99.40 215	87.71 310		97.94 280

原ACMM2							92.82 300

testdata2													99.46 194	87.84 309

segment_acmp												95.34 130

testdata1							92.77 301		93.78 211

plane_prior7					98.70 121	94.67 136

plane_prior6					98.38 158	94.37 146						91.91 225

plane_prior4											96.77 240

plane_prior3						94.51 140			95.29 159	96.16 232

plane_prior2							96.50 131		96.36 105

plane_prior1					98.49 148

n2								0.00 379

nn								0.00 379

door-mid								98.17 209

test11								98.08 222

door								97.81 241

HQP5-MVS						92.47 201

HQP-NCC					97.85 213		94.26 250		93.18 229	92.86 324

ACMP_Plane					97.85 213		94.26 250		93.18 229	92.86 324

BP-MVS														90.51 273

HQP4-MVS										92.87 323			99.23 257			99.06 161

HQP2-MVS												90.33 242

NP-MVS					98.14 187	93.72 172					95.08 305

MDTV_nov1_ep13_2view						57.28 374	94.89 230		80.59 349	94.02 292		78.66 319		85.50 332		97.82 286

Test By Simon												94.51 159

High-resolution multi-view benchmark