High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

test_part1		99.41 2	99.62 2	98.80 31	99.76 5	96.58 57	99.49 3	99.65 2	99.89 2	99.94 2	99.77 2	99.03 4	99.92 4	99.05 3	99.99 2	99.90 1

PS-MVSNAJss		98.53 20	98.63 20	98.21 75	99.68 10	94.82 121	98.10 43	99.21 12	96.91 81	99.75 3	99.45 10	95.82 106	99.92 4	98.80 5	99.96 5	99.89 2

test_djsdf		98.73 12	98.74 17	98.69 41	99.63 13	96.30 66	98.67 12	99.02 50	96.50 93	99.32 21	99.44 11	97.43 31	99.92 4	98.73 8	99.95 6	99.86 3

UA-Net		98.88 8	98.76 14	99.22 2	99.11 82	97.89 13	99.47 4	99.32 8	99.08 11	97.87 135	99.67 3	96.47 84	99.92 4	97.88 23	99.98 3	99.85 4

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 4

mvs_tets		98.90 6	98.94 7	98.75 34	99.69 9	96.48 60	98.54 19	99.22 11	96.23 104	99.71 5	99.48 8	98.77 7	99.93 2	98.89 4	99.95 6	99.84 6

jajsoiax		98.77 10	98.79 13	98.74 36	99.66 11	96.48 60	98.45 24	99.12 26	95.83 129	99.67 7	99.37 13	98.25 11	99.92 4	98.77 6	99.94 9	99.82 7

PS-CasMVS		98.73 12	98.85 11	98.39 60	99.55 18	95.47 97	98.49 21	99.13 25	99.22 9	99.22 27	98.96 41	97.35 34	99.92 4	97.79 28	99.93 11	99.79 8

UniMVSNet_ETH3D		99.12 4	99.28 4	98.65 44	99.77 3	96.34 64	99.18 6	99.20 14	99.67 3	99.73 4	99.65 5	99.15 3	99.86 21	97.22 45	99.92 13	99.77 9

anonymousdsp		98.72 15	98.63 20	98.99 13	99.62 14	97.29 37	98.65 15	99.19 16	95.62 136	99.35 20	99.37 13	97.38 33	99.90 14	98.59 12	99.91 16	99.77 9

FC-MVSNet-test		98.16 34	98.37 28	97.56 117	99.49 27	93.10 182	98.35 27	99.21 12	98.43 28	98.89 39	98.83 50	94.30 160	99.81 32	97.87 24	99.91 16	99.77 9

CP-MVSNet		98.42 24	98.46 25	98.30 68	99.46 29	95.22 110	98.27 32	98.84 93	99.05 14	99.01 35	98.65 63	95.37 126	99.90 14	97.57 34	99.91 16	99.77 9

ANet_high		98.31 29	98.94 7	96.41 198	99.33 43	89.64 239	97.92 52	99.56 5	99.27 7	99.66 9	99.50 7	97.67 26	99.83 29	97.55 35	99.98 3	99.77 9

PEN-MVS		98.75 11	98.85 11	98.44 56	99.58 15	95.67 87	98.45 24	99.15 22	99.33 6	99.30 22	99.00 37	97.27 38	99.92 4	97.64 33	99.92 13	99.75 14

WR-MVS_H		98.65 16	98.62 22	98.75 34	99.51 23	96.61 55	98.55 18	99.17 17	99.05 14	99.17 29	98.79 51	95.47 123	99.89 17	97.95 21	99.91 16	99.75 14

Anonymous20231211		98.55 18	98.76 14	97.94 93	98.79 106	94.37 138	98.84 9	99.15 22	99.37 4	99.67 7	99.43 12	95.61 118	99.72 77	98.12 17	99.86 24	99.73 16

FIs		97.93 55	98.07 37	97.48 129	99.38 39	92.95 185	98.03 48	99.11 27	98.04 41	98.62 52	98.66 61	93.75 174	99.78 41	97.23 44	99.84 27	99.73 16

v7n		98.73 12	98.99 6	97.95 92	99.64 12	94.20 146	98.67 12	99.14 24	99.08 11	99.42 16	99.23 22	96.53 79	99.91 13	99.27 2	99.93 11	99.73 16

nrg030		98.54 19	98.62 22	98.32 65	99.22 57	95.66 88	97.90 53	99.08 35	98.31 32	99.02 34	98.74 55	97.68 25	99.61 146	97.77 29	99.85 26	99.70 19

DTE-MVSNet		98.79 9	98.86 9	98.59 48	99.55 18	96.12 71	98.48 23	99.10 29	99.36 5	99.29 23	99.06 36	97.27 38	99.93 2	97.71 32	99.91 16	99.70 19

RRT_test8_iter05		92.46 276	92.52 272	92.29 315	95.33 324	77.43 345	95.73 168	98.55 156	94.41 181	97.46 155	97.72 164	57.44 358	99.74 67	96.92 57	99.14 187	99.69 21

LTVRE_ROB	96.88 1	99.18 3	99.34 3	98.72 39	99.71 8	96.99 44	99.69 2	99.57 4	99.02 16	99.62 11	99.36 15	98.53 8	99.52 170	98.58 13	99.95 6	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
Baseline_NR-MVSNet		97.72 74	97.79 54	97.50 125	99.56 16	93.29 176	95.44 183	98.86 85	98.20 37	98.37 74	99.24 21	94.69 145	99.55 161	95.98 87	99.79 34	99.65 23

OurMVSNet-221017-0		98.61 17	98.61 24	98.63 46	99.77 3	96.35 63	99.17 7	99.05 41	98.05 40	99.61 12	99.52 6	93.72 175	99.88 19	98.72 10	99.88 22	99.65 23

pmmvs6		99.07 5	99.24 5	98.56 50	99.81 2	96.38 62	98.87 8	99.30 9	99.01 17	99.63 10	99.66 4	99.27 2	99.68 115	97.75 30	99.89 21	99.62 25

TransMVSNet (Re)		98.38 26	98.67 18	97.51 122	99.51 23	93.39 175	98.20 38	98.87 83	98.23 35	99.48 13	99.27 20	98.47 9	99.55 161	96.52 65	99.53 95	99.60 26

XXY-MVS		97.54 86	97.70 62	97.07 157	99.46 29	92.21 198	97.22 93	99.00 58	94.93 166	98.58 57	98.92 45	97.31 36	99.41 204	94.44 162	99.43 132	99.59 27

test_0728_THIRD									96.62 88	98.40 71	98.28 93	97.10 45	99.71 92	95.70 93	99.62 64	99.58 28

MSP-MVS		97.45 93	96.92 121	99.03 8	99.26 48	97.70 18	97.66 65	98.89 76	95.65 134	98.51 61	96.46 252	92.15 210	99.81 32	95.14 133	98.58 251	99.58 28

EI-MVSNet-UG-set		97.32 104	97.40 89	97.09 156	97.34 264	92.01 206	95.33 194	97.65 244	97.74 50	98.30 87	98.14 109	95.04 136	99.69 109	97.55 35	99.52 100	99.58 28

v10		97.55 85	97.97 42	96.31 202	98.60 131	89.64 239	97.44 81	99.02 50	96.60 89	98.72 50	99.16 30	93.48 179	99.72 77	98.76 7	99.92 13	99.58 28

APDe-MVS		98.14 35	98.03 41	98.47 55	98.72 114	96.04 73	98.07 45	99.10 29	95.96 118	98.59 56	98.69 59	96.94 55	99.81 32	96.64 60	99.58 77	99.57 32

EI-MVSNet-Vis-set		97.32 104	97.39 90	97.11 154	97.36 259	92.08 204	95.34 193	97.65 244	97.74 50	98.29 88	98.11 114	95.05 134	99.68 115	97.50 37	99.50 107	99.56 33

v8		97.60 82	98.06 39	96.23 204	98.71 117	89.44 243	97.43 83	98.82 108	97.29 75	98.74 48	99.10 33	93.86 170	99.68 115	98.61 11	99.94 9	99.56 33

VPA-MVSNet		98.27 30	98.46 25	97.70 108	99.06 87	93.80 160	97.76 60	99.00 58	98.40 29	99.07 33	98.98 39	96.89 60	99.75 60	97.19 49	99.79 34	99.55 35

WR-MVS		96.90 123	96.81 126	97.16 151	98.56 136	92.20 200	94.33 242	98.12 210	97.34 72	98.20 94	97.33 198	92.81 192	99.75 60	94.79 149	99.81 30	99.54 36

TranMVSNet+NR-MVSNet		98.33 27	98.30 32	98.43 57	99.07 86	95.87 78	96.73 118	99.05 41	98.67 23	98.84 41	98.45 76	97.58 28	99.88 19	96.45 69	99.86 24	99.54 36

SixPastTwentyTwo		97.49 90	97.57 80	97.26 148	99.56 16	92.33 194	98.28 30	96.97 270	98.30 33	99.45 15	99.35 17	88.43 260	99.89 17	98.01 20	99.76 38	99.54 36

test_0728_SECOND				98.25 72	99.23 54	95.49 96	96.74 114	98.89 76					99.75 60	95.48 108	99.52 100	99.53 39

DPE-MVS		97.64 78	97.35 93	98.50 52	98.85 101	96.18 68	95.21 205	98.99 61	95.84 128	98.78 44	98.08 116	96.84 65	99.81 32	93.98 186	99.57 80	99.52 40

VPNet		97.26 107	97.49 86	96.59 185	99.47 28	90.58 230	96.27 136	98.53 158	97.77 46	98.46 67	98.41 78	94.59 151	99.68 115	94.61 155	99.29 172	99.52 40

Regformer-4		97.53 88	97.47 88	97.71 106	97.35 260	93.91 154	95.26 200	98.14 207	97.97 42	98.34 79	97.89 143	95.49 121	99.71 92	97.41 39	99.42 135	99.51 42

v1192		96.83 129	97.06 113	96.15 209	98.28 162	89.29 245	95.36 191	98.77 115	93.73 203	98.11 105	98.34 83	93.02 190	99.67 120	98.35 15	99.58 77	99.50 43

pm-mvs1		98.47 22	98.67 18	97.86 98	99.52 22	94.58 131	98.28 30	99.00 58	97.57 60	99.27 24	99.22 23	98.32 10	99.50 175	97.09 52	99.75 42	99.50 43

EI-MVSNet		96.63 144	96.93 120	95.74 224	97.26 269	88.13 267	95.29 198	97.65 244	96.99 78	97.94 126	98.19 106	92.55 201	99.58 150	96.91 58	99.56 83	99.50 43

HPM-MVS		98.11 39	97.83 52	98.92 22	99.42 35	97.46 31	98.57 16	99.05 41	95.43 145	97.41 158	97.50 180	97.98 16	99.79 39	95.58 104	99.57 80	99.50 43
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
LPG-MVS_test		97.94 52	97.67 65	98.74 36	99.15 72	97.02 42	97.09 99	99.02 50	95.15 155	98.34 79	98.23 101	97.91 18	99.70 101	94.41 164	99.73 44	99.50 43

LGP-MVS_train				98.74 36	99.15 72	97.02 42		99.02 50	95.15 155	98.34 79	98.23 101	97.91 18	99.70 101	94.41 164	99.73 44	99.50 43

IterMVS-LS		96.92 121	97.29 96	95.79 223	98.51 141	88.13 267	95.10 208	98.66 143	96.99 78	98.46 67	98.68 60	92.55 201	99.74 67	96.91 58	99.79 34	99.50 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	93.61 9	98.44 23	98.76 14	97.51 122	99.43 33	93.54 171	98.23 33	99.05 41	97.40 71	99.37 19	99.08 35	98.79 6	99.47 182	97.74 31	99.71 50	99.50 43
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IU-MVS					99.22 57	95.40 98		98.14 207	85.77 307	98.36 76				95.23 125	99.51 105	99.49 51

test_241102_TWO								98.83 100	96.11 108	98.62 52	98.24 99	96.92 58	99.72 77	95.44 112	99.49 111	99.49 51

v1921920		96.72 137	96.96 119	95.99 213	98.21 171	88.79 255	95.42 185	98.79 110	93.22 218	98.19 97	98.26 98	92.68 196	99.70 101	98.34 16	99.55 89	99.49 51

v1240		96.74 134	97.02 116	95.91 220	98.18 176	88.52 258	95.39 189	98.88 81	93.15 224	98.46 67	98.40 80	92.80 193	99.71 92	98.45 14	99.49 111	99.49 51

ACMMPR		97.95 50	97.62 75	98.94 18	99.20 65	97.56 25	97.59 70	98.83 100	96.05 111	97.46 155	97.63 170	96.77 67	99.76 53	95.61 101	99.46 120	99.49 51

MP-MVS-pluss		97.69 76	97.36 92	98.70 40	99.50 26	96.84 47	95.38 190	98.99 61	92.45 239	98.11 105	98.31 86	97.25 41	99.77 49	96.60 61	99.62 64	99.48 56
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PGM-MVS		97.88 61	97.52 83	98.96 16	99.20 65	97.62 21	97.09 99	99.06 39	95.45 143	97.55 144	97.94 138	97.11 44	99.78 41	94.77 152	99.46 120	99.48 56

UniMVSNet_NR-MVSNet		97.83 66	97.65 68	98.37 61	98.72 114	95.78 80	95.66 174	99.02 50	98.11 39	98.31 85	97.69 167	94.65 149	99.85 23	97.02 55	99.71 50	99.48 56

v144192		96.69 140	96.90 123	96.03 212	98.25 167	88.92 250	95.49 181	98.77 115	93.05 226	98.09 109	98.29 92	92.51 205	99.70 101	98.11 18	99.56 83	99.47 59

MIMVSNet1		98.51 21	98.45 27	98.67 42	99.72 7	96.71 50	98.76 10	98.89 76	98.49 27	99.38 18	99.14 31	95.44 125	99.84 26	96.47 68	99.80 33	99.47 59

region2R		97.92 56	97.59 78	98.92 22	99.22 57	97.55 26	97.60 69	98.84 93	96.00 116	97.22 162	97.62 171	96.87 63	99.76 53	95.48 108	99.43 132	99.46 61

Regformer-3		97.25 108	97.29 96	97.11 154	97.35 260	92.32 195	95.26 200	97.62 249	97.67 58	98.17 98	97.89 143	95.05 134	99.56 157	97.16 50	99.42 135	99.46 61

DU-MVS		97.79 70	97.60 77	98.36 62	98.73 112	95.78 80	95.65 176	98.87 83	97.57 60	98.31 85	97.83 150	94.69 145	99.85 23	97.02 55	99.71 50	99.46 61

NR-MVSNet		97.96 47	97.86 49	98.26 70	98.73 112	95.54 92	98.14 41	98.73 123	97.79 45	99.42 16	97.83 150	94.40 158	99.78 41	95.91 90	99.76 38	99.46 61

mPP-MVS		97.91 59	97.53 82	99.04 7	99.22 57	97.87 14	97.74 62	98.78 114	96.04 113	97.10 170	97.73 162	96.53 79	99.78 41	95.16 130	99.50 107	99.46 61

ZNCC-MVS		97.92 56	97.62 75	98.83 26	99.32 45	97.24 39	97.45 80	98.84 93	95.76 131	96.93 185	97.43 185	97.26 40	99.79 39	96.06 78	99.53 95	99.45 66

SMA-MVS		97.48 91	97.11 108	98.60 47	98.83 102	96.67 52	96.74 114	98.73 123	91.61 250	98.48 64	98.36 81	96.53 79	99.68 115	95.17 128	99.54 92	99.45 66
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
ACMMP_NAP		97.89 60	97.63 73	98.67 42	99.35 42	96.84 47	96.36 132	98.79 110	95.07 159	97.88 132	98.35 82	97.24 42	99.72 77	96.05 80	99.58 77	99.45 66

zzz-MVS		98.01 45	97.66 66	99.06 4	99.44 31	97.90 11	95.66 174	98.73 123	97.69 56	97.90 129	97.96 133	95.81 110	99.82 30	96.13 76	99.61 70	99.45 66

MTAPA		98.14 35	97.84 50	99.06 4	99.44 31	97.90 11	97.25 90	98.73 123	97.69 56	97.90 129	97.96 133	95.81 110	99.82 30	96.13 76	99.61 70	99.45 66

v1144		96.84 126	97.08 111	96.13 210	98.42 152	89.28 246	95.41 187	98.67 141	94.21 189	97.97 123	98.31 86	93.06 186	99.65 127	98.06 19	99.62 64	99.45 66

XVS		97.96 47	97.63 73	98.94 18	99.15 72	97.66 19	97.77 58	98.83 100	97.42 67	96.32 213	97.64 169	96.49 82	99.72 77	95.66 97	99.37 146	99.45 66

X-MVStestdata		92.86 270	90.83 295	98.94 18	99.15 72	97.66 19	97.77 58	98.83 100	97.42 67	96.32 213	36.50 355	96.49 82	99.72 77	95.66 97	99.37 146	99.45 66

v2v482		96.78 133	97.06 113	95.95 217	98.57 135	88.77 256	95.36 191	98.26 190	95.18 154	97.85 137	98.23 101	92.58 200	99.63 132	97.80 27	99.69 54	99.45 66

MP-MVS		97.64 78	97.18 105	99.00 12	99.32 45	97.77 17	97.49 79	98.73 123	96.27 101	95.59 244	97.75 159	96.30 93	99.78 41	93.70 196	99.48 115	99.45 66
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
EU-MVSNet		94.25 237	94.47 224	93.60 289	98.14 183	82.60 329	97.24 92	92.72 323	85.08 315	98.48 64	98.94 43	82.59 295	98.76 299	97.47 38	99.53 95	99.44 76

ACMMP		98.05 42	97.75 60	98.93 21	99.23 54	97.60 22	98.09 44	98.96 68	95.75 133	97.91 128	98.06 123	96.89 60	99.76 53	95.32 119	99.57 80	99.43 77
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
GST-MVS		97.82 68	97.49 86	98.81 29	99.23 54	97.25 38	97.16 94	98.79 110	95.96 118	97.53 145	97.40 187	96.93 57	99.77 49	95.04 139	99.35 154	99.42 78

HPM-MVS_fast		98.32 28	98.13 34	98.88 24	99.54 20	97.48 30	98.35 27	99.03 48	95.88 124	97.88 132	98.22 104	98.15 13	99.74 67	96.50 67	99.62 64	99.42 78

UniMVSNet (Re)		97.83 66	97.65 68	98.35 64	98.80 105	95.86 79	95.92 162	99.04 47	97.51 64	98.22 93	97.81 154	94.68 147	99.78 41	97.14 51	99.75 42	99.41 80

testing_2		97.43 95	97.71 61	96.60 183	98.91 98	90.85 223	96.01 154	98.54 157	94.78 169	98.78 44	98.96 41	96.35 92	99.54 164	97.25 43	99.82 29	99.40 81

SteuartSystems-ACMMP		98.02 44	97.76 58	98.79 32	99.43 33	97.21 41	97.15 95	98.90 75	96.58 91	98.08 111	97.87 147	97.02 53	99.76 53	95.25 123	99.59 75	99.40 81
Skip Steuart: Steuart Systems R&D Blog.
TDRefinement		98.90 6	98.86 9	99.02 9	99.54 20	98.06 7	99.34 5	99.44 7	98.85 20	99.00 36	99.20 24	97.42 32	99.59 148	97.21 46	99.76 38	99.40 81

K. test v3		96.44 152	96.28 153	96.95 162	99.41 36	91.53 214	97.65 66	90.31 342	98.89 19	98.93 38	99.36 15	84.57 289	99.92 4	97.81 26	99.56 83	99.39 84

ACMM	93.33 11	98.05 42	97.79 54	98.85 25	99.15 72	97.55 26	96.68 120	98.83 100	95.21 151	98.36 76	98.13 110	98.13 15	99.62 140	96.04 81	99.54 92	99.39 84
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
V42		97.04 113	97.16 106	96.68 181	98.59 133	91.05 219	96.33 134	98.36 179	94.60 175	97.99 119	98.30 90	93.32 181	99.62 140	97.40 40	99.53 95	99.38 86

abl_6		98.42 24	98.19 33	99.09 3	99.16 69	98.10 5	97.73 64	99.11 27	97.76 49	98.62 52	98.27 97	97.88 20	99.80 38	95.67 95	99.50 107	99.38 86

CP-MVS		97.92 56	97.56 81	98.99 13	98.99 93	97.82 15	97.93 51	98.96 68	96.11 108	96.89 188	97.45 184	96.85 64	99.78 41	95.19 126	99.63 63	99.38 86

EG-PatchMatch MVS		97.69 76	97.79 54	97.40 139	99.06 87	93.52 172	95.96 158	98.97 67	94.55 179	98.82 42	98.76 54	97.31 36	99.29 238	97.20 48	99.44 125	99.38 86

IS-MVSNet		96.93 120	96.68 133	97.70 108	99.25 51	94.00 152	98.57 16	96.74 278	98.36 30	98.14 103	97.98 132	88.23 262	99.71 92	93.10 209	99.72 47	99.38 86

Regformer-2		97.41 97	97.24 101	97.93 94	97.21 271	94.72 124	94.85 226	98.27 188	97.74 50	98.11 105	97.50 180	95.58 119	99.69 109	96.57 64	99.31 168	99.37 91

UGNet		96.81 131	96.56 139	97.58 116	96.64 287	93.84 159	97.75 61	97.12 265	96.47 96	93.62 292	98.88 47	93.22 184	99.53 166	95.61 101	99.69 54	99.36 92
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
Regformer-1		97.27 106	97.16 106	97.61 115	97.21 271	93.86 157	94.85 226	98.04 221	97.62 59	98.03 117	97.50 180	95.34 127	99.63 132	96.52 65	99.31 168	99.35 93

VDDNet		96.98 118	96.84 124	97.41 138	99.40 37	93.26 177	97.94 50	95.31 300	99.26 8	98.39 73	99.18 28	87.85 269	99.62 140	95.13 135	99.09 198	99.35 93

test1172		98.08 40	97.76 58	99.05 6	98.78 108	98.07 6	97.41 85	98.85 89	97.57 60	98.15 101	97.96 133	96.60 76	99.76 53	95.30 120	99.18 184	99.33 95

SR-MVS		98.00 46	97.66 66	99.01 11	98.77 110	97.93 10	97.38 86	98.83 100	97.32 73	98.06 113	97.85 148	96.65 71	99.77 49	95.00 142	99.11 195	99.32 96

APD-MVS_3200maxsize		98.13 38	97.90 45	98.79 32	98.79 106	97.31 36	97.55 73	98.92 73	97.72 53	98.25 90	98.13 110	97.10 45	99.75 60	95.44 112	99.24 178	99.32 96

EPP-MVSNet		96.84 126	96.58 137	97.65 112	99.18 68	93.78 162	98.68 11	96.34 282	97.91 44	97.30 160	98.06 123	88.46 259	99.85 23	93.85 190	99.40 142	99.32 96

ACMP	92.54 13	97.47 92	97.10 109	98.55 51	99.04 90	96.70 51	96.24 140	98.89 76	93.71 204	97.97 123	97.75 159	97.44 30	99.63 132	93.22 206	99.70 53	99.32 96
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH+	93.58 10	98.23 33	98.31 30	97.98 91	99.39 38	95.22 110	97.55 73	99.20 14	98.21 36	99.25 25	98.51 72	98.21 12	99.40 206	94.79 149	99.72 47	99.32 96

testtj		96.69 140	96.13 158	98.36 62	98.46 150	96.02 75	96.44 126	98.70 133	94.26 187	96.79 190	97.13 208	94.07 166	99.75 60	90.53 260	98.80 231	99.31 101

HFP-MVS		97.94 52	97.64 71	98.83 26	99.15 72	97.50 28	97.59 70	98.84 93	96.05 111	97.49 149	97.54 175	97.07 48	99.70 101	95.61 101	99.46 120	99.30 102

#test#		97.62 80	97.22 103	98.83 26	99.15 72	97.50 28	96.81 110	98.84 93	94.25 188	97.49 149	97.54 175	97.07 48	99.70 101	94.37 167	99.46 120	99.30 102

lessismore_v0				97.05 158	99.36 41	92.12 202		84.07 354		98.77 47	98.98 39	85.36 283	99.74 67	97.34 42	99.37 146	99.30 102

GBi-Net		96.99 115	96.80 127	97.56 117	97.96 199	93.67 165	98.23 33	98.66 143	95.59 138	97.99 119	99.19 25	89.51 251	99.73 73	94.60 156	99.44 125	99.30 102

test1		96.99 115	96.80 127	97.56 117	97.96 199	93.67 165	98.23 33	98.66 143	95.59 138	97.99 119	99.19 25	89.51 251	99.73 73	94.60 156	99.44 125	99.30 102

FMVSNet1		97.95 50	98.08 36	97.56 117	99.14 80	93.67 165	98.23 33	98.66 143	97.41 70	99.00 36	99.19 25	95.47 123	99.73 73	95.83 91	99.76 38	99.30 102

v148		96.58 146	96.97 117	95.42 238	98.63 127	87.57 278	95.09 210	97.90 225	95.91 123	98.24 92	97.96 133	93.42 180	99.39 211	96.04 81	99.52 100	99.29 108

TSAR-MVS + MP.		97.42 96	97.23 102	98.00 90	99.38 39	95.00 116	97.63 68	98.20 197	93.00 227	98.16 99	98.06 123	95.89 101	99.72 77	95.67 95	99.10 197	99.28 109
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
casdiffmvs		97.50 89	97.81 53	96.56 189	98.51 141	91.04 220	95.83 166	99.09 34	97.23 76	98.33 82	98.30 90	97.03 52	99.37 217	96.58 63	99.38 145	99.28 109

HQP_MVS		96.66 143	96.33 152	97.68 111	98.70 119	94.29 140	96.50 124	98.75 119	96.36 98	96.16 223	96.77 234	91.91 221	99.46 185	92.59 215	99.20 180	99.28 109

plane_prior5								98.75 119					99.46 185	92.59 215	99.20 180	99.28 109

IterMVS-SCA-FT		95.86 174	96.19 156	94.85 259	97.68 237	85.53 304	92.42 303	97.63 248	96.99 78	98.36 76	98.54 70	87.94 264	99.75 60	97.07 54	99.08 199	99.27 113

CL-MVSNet_2432*1600		97.86 64	98.07 37	97.25 149	99.22 57	92.81 187	97.55 73	98.94 71	97.10 77	98.85 40	98.88 47	95.03 137	99.67 120	97.39 41	99.65 60	99.26 114

SR-MVS-dyc-post		98.14 35	97.84 50	99.02 9	98.81 103	98.05 8	97.55 73	98.86 85	97.77 46	98.20 94	98.07 118	96.60 76	99.76 53	95.49 105	99.20 180	99.26 114

RE-MVS-def			97.88 48		98.81 103	98.05 8	97.55 73	98.86 85	97.77 46	98.20 94	98.07 118	96.94 55		95.49 105	99.20 180	99.26 114

DVP-MVS		97.78 71	97.65 68	98.16 76	99.24 52	95.51 94	96.74 114	98.23 193	95.92 121	98.40 71	98.28 93	97.06 50	99.71 92	95.48 108	99.52 100	99.26 114

xxxxxxxxxxxxxcwj		97.24 109	97.03 115	97.89 96	98.48 146	94.71 125	94.53 238	99.07 38	95.02 162	97.83 138	97.88 145	96.44 86	99.72 77	94.59 159	99.39 143	99.25 118

SF-MVS		97.60 82	97.39 90	98.22 74	98.93 96	95.69 84	97.05 101	99.10 29	95.32 148	97.83 138	97.88 145	96.44 86	99.72 77	94.59 159	99.39 143	99.25 118

3Dnovator+	96.13 3	97.73 73	97.59 78	98.15 79	98.11 188	95.60 90	98.04 46	98.70 133	98.13 38	96.93 185	98.45 76	95.30 130	99.62 140	95.64 99	98.96 210	99.24 120

Anonymous20240529		97.96 47	98.04 40	97.71 106	98.69 121	94.28 143	97.86 55	98.31 187	98.79 21	99.23 26	98.86 49	95.76 113	99.61 146	95.49 105	99.36 149	99.23 121

IterMVS		95.42 191	95.83 172	94.20 282	97.52 249	83.78 325	92.41 304	97.47 255	95.49 142	98.06 113	98.49 73	87.94 264	99.58 150	96.02 83	99.02 206	99.23 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OPM-MVS		97.54 86	97.25 99	98.41 58	99.11 82	96.61 55	95.24 203	98.46 164	94.58 178	98.10 108	98.07 118	97.09 47	99.39 211	95.16 130	99.44 125	99.21 123

EPNet		93.72 253	92.62 270	97.03 160	87.61 359	92.25 196	96.27 136	91.28 333	96.74 86	87.65 347	97.39 191	85.00 285	99.64 130	92.14 219	99.48 115	99.20 124
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
baseline		97.44 94	97.78 57	96.43 195	98.52 140	90.75 228	96.84 108	99.03 48	96.51 92	97.86 136	98.02 127	96.67 70	99.36 219	97.09 52	99.47 117	99.19 125

APD-MVS		97.00 114	96.53 143	98.41 58	98.55 137	96.31 65	96.32 135	98.77 115	92.96 232	97.44 157	97.58 174	95.84 103	99.74 67	91.96 220	99.35 154	99.19 125
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CNVR-MVS		96.92 121	96.55 140	98.03 89	98.00 197	95.54 92	94.87 224	98.17 203	94.60 175	96.38 210	97.05 216	95.67 116	99.36 219	95.12 136	99.08 199	99.19 125

ETH3D-3000-0.1		96.89 125	96.46 147	98.16 76	98.62 128	95.69 84	95.96 158	98.98 64	93.36 212	97.04 176	97.31 200	94.93 141	99.63 132	92.60 213	99.34 157	99.17 128

NCCC		96.52 148	95.99 166	98.10 81	97.81 215	95.68 86	95.00 219	98.20 197	95.39 146	95.40 248	96.36 257	93.81 172	99.45 189	93.55 199	98.42 256	99.17 128

CPTT-MVS		96.69 140	96.08 162	98.49 53	98.89 100	96.64 54	97.25 90	98.77 115	92.89 233	96.01 229	97.13 208	92.23 209	99.67 120	92.24 218	99.34 157	99.17 128

RPSCF		97.87 62	97.51 84	98.95 17	99.15 72	98.43 3	97.56 72	99.06 39	96.19 105	98.48 64	98.70 58	94.72 144	99.24 246	94.37 167	99.33 164	99.17 128

Vis-MVSNet		98.27 30	98.34 29	98.07 83	99.33 43	95.21 112	98.04 46	99.46 6	97.32 73	97.82 140	99.11 32	96.75 68	99.86 21	97.84 25	99.36 149	99.15 132
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS_111021_HR		96.73 136	96.54 142	97.27 146	98.35 157	93.66 168	93.42 280	98.36 179	94.74 170	96.58 200	96.76 236	96.54 78	98.99 277	94.87 145	99.27 175	99.15 132

DeepC-MVS	95.41 4	97.82 68	97.70 62	98.16 76	98.78 108	95.72 82	96.23 141	99.02 50	93.92 200	98.62 52	98.99 38	97.69 24	99.62 140	96.18 75	99.87 23	99.15 132
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SED-MVS		97.94 52	97.90 45	98.07 83	99.22 57	95.35 102	96.79 111	98.83 100	96.11 108	99.08 31	98.24 99	97.87 21	99.72 77	95.44 112	99.51 105	99.14 135

OPU-MVS				97.64 113	98.01 193	95.27 105	96.79 111				97.35 196	96.97 54	98.51 320	91.21 238	99.25 177	99.14 135

RRT_MVS		94.90 210	94.07 237	97.39 140	93.18 346	93.21 179	95.26 200	97.49 252	93.94 199	98.25 90	97.85 148	72.96 339	99.84 26	97.90 22	99.78 37	99.14 135

HPM-MVS++		96.99 115	96.38 149	98.81 29	98.64 123	97.59 23	95.97 157	98.20 197	95.51 141	95.06 253	96.53 248	94.10 165	99.70 101	94.29 171	99.15 186	99.13 138

MCST-MVS		96.24 157	95.80 173	97.56 117	98.75 111	94.13 148	94.66 233	98.17 203	90.17 266	96.21 221	96.10 270	95.14 133	99.43 194	94.13 178	98.85 227	99.13 138

UnsupCasMVSNet_eth		95.91 171	95.73 176	96.44 194	98.48 146	91.52 215	95.31 196	98.45 165	95.76 131	97.48 152	97.54 175	89.53 250	98.69 305	94.43 163	94.61 333	99.13 138

3Dnovator	96.53 2	97.61 81	97.64 71	97.50 125	97.74 233	93.65 169	98.49 21	98.88 81	96.86 83	97.11 169	98.55 69	95.82 106	99.73 73	95.94 88	99.42 135	99.13 138

COLMAP_ROB	94.48 6	98.25 32	98.11 35	98.64 45	99.21 64	97.35 35	97.96 49	99.16 18	98.34 31	98.78 44	98.52 71	97.32 35	99.45 189	94.08 179	99.67 57	99.13 138
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
new-patchmatchnet		95.67 179	96.58 137	92.94 306	97.48 250	80.21 337	92.96 291	98.19 202	94.83 167	98.82 42	98.79 51	93.31 182	99.51 174	95.83 91	99.04 205	99.12 143

VDD-MVS		97.37 100	97.25 99	97.74 104	98.69 121	94.50 134	97.04 102	95.61 295	98.59 25	98.51 61	98.72 56	92.54 203	99.58 150	96.02 83	99.49 111	99.12 143

MVSTER		94.21 240	93.93 243	95.05 250	95.83 312	86.46 294	95.18 206	97.65 244	92.41 240	97.94 126	98.00 131	72.39 340	99.58 150	96.36 71	99.56 83	99.12 143

testgi		96.07 164	96.50 146	94.80 262	99.26 48	87.69 277	95.96 158	98.58 154	95.08 158	98.02 118	96.25 261	97.92 17	97.60 343	88.68 290	98.74 237	99.11 146

CDPH-MVS		95.45 190	94.65 212	97.84 100	98.28 162	94.96 117	93.73 272	98.33 184	85.03 317	95.44 246	96.60 244	95.31 129	99.44 192	90.01 270	99.13 191	99.11 146

PVSNet_BlendedMVS		95.02 208	94.93 200	95.27 242	97.79 225	87.40 282	94.14 255	98.68 138	88.94 277	94.51 266	98.01 129	93.04 187	99.30 234	89.77 274	99.49 111	99.11 146

DP-MVS		97.87 62	97.89 47	97.81 101	98.62 128	94.82 121	97.13 98	98.79 110	98.98 18	98.74 48	98.49 73	95.80 112	99.49 176	95.04 139	99.44 125	99.11 146

agg_prior2														90.34 267	98.90 219	99.10 150

VNet		96.84 126	96.83 125	96.88 167	98.06 189	92.02 205	96.35 133	97.57 251	97.70 55	97.88 132	97.80 155	92.40 207	99.54 164	94.73 154	98.96 210	99.08 151

CHOSEN 1792x2688		94.10 244	93.41 251	96.18 208	99.16 69	90.04 235	92.15 307	98.68 138	79.90 339	96.22 220	97.83 150	87.92 268	99.42 195	89.18 282	99.65 60	99.08 151

XVG-OURS-SEG-HR		97.38 99	97.07 112	98.30 68	99.01 92	97.41 34	94.66 233	99.02 50	95.20 152	98.15 101	97.52 178	98.83 5	98.43 323	94.87 145	96.41 317	99.07 153

FMVSNet2		96.72 137	96.67 134	96.87 168	97.96 199	91.88 208	97.15 95	98.06 219	95.59 138	98.50 63	98.62 64	89.51 251	99.65 127	94.99 143	99.60 73	99.07 153

diffmvs		96.04 166	96.23 154	95.46 237	97.35 260	88.03 269	93.42 280	99.08 35	94.09 195	96.66 197	96.93 223	93.85 171	99.29 238	96.01 85	98.67 242	99.06 155

HQP4-MVS										92.87 310			99.23 248			99.06 155

ETH3 D test6400		94.77 216	93.87 244	97.47 130	98.12 187	93.73 163	94.56 237	98.70 133	85.45 312	94.70 261	95.93 279	91.77 223	99.63 132	86.45 313	99.14 187	99.05 157

HQP-MVS		95.17 202	94.58 220	96.92 164	97.85 207	92.47 192	94.26 243	98.43 168	93.18 220	92.86 311	95.08 294	90.33 237	99.23 248	90.51 262	98.74 237	99.05 157

FMVSNet5		93.39 262	92.35 273	96.50 191	95.83 312	90.81 227	97.31 87	98.27 188	92.74 235	96.27 217	98.28 93	62.23 355	99.67 120	90.86 245	99.36 149	99.03 159

HyFIR lowres test		93.72 253	92.65 268	96.91 166	98.93 96	91.81 211	91.23 324	98.52 159	82.69 327	96.46 207	96.52 250	80.38 303	99.90 14	90.36 266	98.79 232	99.03 159

tttt0517		93.31 264	92.56 271	95.57 230	98.71 117	87.86 271	97.44 81	87.17 350	95.79 130	97.47 154	96.84 228	64.12 353	99.81 32	96.20 74	99.32 166	99.02 161

test9_res														91.29 234	98.89 222	99.00 162

test20.03		96.58 146	96.61 135	96.48 193	98.49 144	91.72 212	95.68 173	97.69 239	96.81 84	98.27 89	97.92 141	94.18 164	98.71 303	90.78 249	99.66 59	99.00 162

XVG-ACMP-BASELINE		97.58 84	97.28 98	98.49 53	99.16 69	96.90 46	96.39 129	98.98 64	95.05 160	98.06 113	98.02 127	95.86 102	99.56 157	94.37 167	99.64 62	99.00 162

MDA-MVSNet-bldmvs		95.69 177	95.67 177	95.74 224	98.48 146	88.76 257	92.84 292	97.25 258	96.00 116	97.59 143	97.95 137	91.38 226	99.46 185	93.16 208	96.35 318	98.99 165

Vis-MVSNet (Re-imp)		95.11 203	94.85 204	95.87 222	99.12 81	89.17 247	97.54 78	94.92 302	96.50 93	96.58 200	97.27 202	83.64 292	99.48 179	88.42 293	99.67 57	98.97 166

FMVSNet3		95.26 198	94.94 198	96.22 206	96.53 290	90.06 234	95.99 155	97.66 242	94.11 194	97.99 119	97.91 142	80.22 304	99.63 132	94.60 156	99.44 125	98.96 167

ambc				96.56 189	98.23 170	91.68 213	97.88 54	98.13 209		98.42 70	98.56 68	94.22 163	99.04 271	94.05 183	99.35 154	98.95 168

YYNet1		94.73 217	94.84 205	94.41 277	97.47 254	85.09 313	90.29 334	95.85 292	92.52 236	97.53 145	97.76 156	91.97 216	99.18 252	93.31 202	96.86 307	98.95 168

ppachtmachnet_test		94.49 232	94.84 205	93.46 292	96.16 303	82.10 331	90.59 331	97.48 254	90.53 262	97.01 179	97.59 173	91.01 229	99.36 219	93.97 187	99.18 184	98.94 170

CANet		95.86 174	95.65 178	96.49 192	96.41 293	90.82 225	94.36 241	98.41 173	94.94 164	92.62 319	96.73 237	92.68 196	99.71 92	95.12 136	99.60 73	98.94 170

Anonymous20231206		95.27 197	95.06 195	95.88 221	98.72 114	89.37 244	95.70 170	97.85 228	88.00 288	96.98 182	97.62 171	91.95 217	99.34 224	89.21 281	99.53 95	98.94 170

MDA-MVSNet_test_wron		94.73 217	94.83 207	94.42 276	97.48 250	85.15 311	90.28 335	95.87 291	92.52 236	97.48 152	97.76 156	91.92 220	99.17 256	93.32 201	96.80 310	98.94 170

ETH3D cwj APD-0.16		96.23 158	95.61 180	98.09 82	97.91 203	95.65 89	94.94 221	98.74 121	91.31 254	96.02 228	97.08 213	94.05 167	99.69 109	91.51 231	98.94 214	98.93 174

LFMVS		95.32 195	94.88 203	96.62 182	98.03 190	91.47 216	97.65 66	90.72 339	99.11 10	97.89 131	98.31 86	79.20 306	99.48 179	93.91 189	99.12 194	98.93 174

XVG-OURS		97.12 112	96.74 130	98.26 70	98.99 93	97.45 32	93.82 268	99.05 41	95.19 153	98.32 83	97.70 165	95.22 132	98.41 324	94.27 172	98.13 266	98.93 174

DeepPCF-MVS	94.58 5	96.90 123	96.43 148	98.31 67	97.48 250	97.23 40	92.56 300	98.60 151	92.84 234	98.54 59	97.40 187	96.64 73	98.78 296	94.40 166	99.41 141	98.93 174

Anonymous202405211		96.34 155	95.98 167	97.43 136	98.25 167	93.85 158	96.74 114	94.41 307	97.72 53	98.37 74	98.03 126	87.15 273	99.53 166	94.06 180	99.07 201	98.92 178

our_test_3		94.20 242	94.58 220	93.07 300	96.16 303	81.20 334	90.42 333	96.84 273	90.72 260	97.14 166	97.13 208	90.47 235	99.11 263	94.04 184	98.25 262	98.91 179

tfpnnormal		97.72 74	97.97 42	96.94 163	99.26 48	92.23 197	97.83 57	98.45 165	98.25 34	99.13 30	98.66 61	96.65 71	99.69 109	93.92 188	99.62 64	98.91 179

AllTest		97.20 111	96.92 121	98.06 85	99.08 84	96.16 69	97.14 97	99.16 18	94.35 184	97.78 141	98.07 118	95.84 103	99.12 260	91.41 232	99.42 135	98.91 179

TestCases				98.06 85	99.08 84	96.16 69		99.16 18	94.35 184	97.78 141	98.07 118	95.84 103	99.12 260	91.41 232	99.42 135	98.91 179

pmmvs-eth3d		96.49 149	96.18 157	97.42 137	98.25 167	94.29 140	94.77 230	98.07 218	89.81 269	97.97 123	98.33 84	93.11 185	99.08 267	95.46 111	99.84 27	98.89 183

train_agg		95.46 189	94.66 211	97.88 97	97.84 212	95.23 107	93.62 274	98.39 175	87.04 295	93.78 284	95.99 272	94.58 152	99.52 170	91.76 227	98.90 219	98.89 183

test12				97.46 132	97.61 244	94.07 149		97.78 234		93.57 295		93.31 182	99.42 195		98.78 233	98.89 183

pmmvs5		94.63 227	94.34 229	95.50 234	97.63 243	88.34 262	94.02 259	97.13 264	87.15 294	95.22 251	97.15 207	87.50 270	99.27 242	93.99 185	99.26 176	98.88 186

DeepC-MVS_fast	94.34 7	96.74 134	96.51 145	97.44 135	97.69 236	94.15 147	96.02 152	98.43 168	93.17 223	97.30 160	97.38 193	95.48 122	99.28 240	93.74 193	99.34 157	98.88 186
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SD-MVS		97.37 100	97.70 62	96.35 199	98.14 183	95.13 113	96.54 123	98.92 73	95.94 120	99.19 28	98.08 116	97.74 23	95.06 351	95.24 124	99.54 92	98.87 188
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
PMMVS2		93.66 256	94.07 237	92.45 312	97.57 245	80.67 336	86.46 347	96.00 287	93.99 197	97.10 170	97.38 193	89.90 245	97.82 340	88.76 287	99.47 117	98.86 189

PVSNet_Blended_VisFu		95.95 170	95.80 173	96.42 196	99.28 47	90.62 229	95.31 196	99.08 35	88.40 283	96.97 183	98.17 108	92.11 212	99.78 41	93.64 197	99.21 179	98.86 189

miper_lstm_enhance		94.81 215	94.80 208	94.85 259	96.16 303	86.45 295	91.14 326	98.20 197	93.49 208	97.03 177	97.37 195	84.97 286	99.26 243	95.28 121	99.56 83	98.83 191

PHI-MVS		96.96 119	96.53 143	98.25 72	97.48 250	96.50 59	96.76 113	98.85 89	93.52 207	96.19 222	96.85 227	95.94 100	99.42 195	93.79 192	99.43 132	98.83 191

QAPM		95.88 173	95.57 182	96.80 172	97.90 205	91.84 210	98.18 40	98.73 123	88.41 282	96.42 208	98.13 110	94.73 143	99.75 60	88.72 288	98.94 214	98.81 193

Patchmtry		95.03 207	94.59 219	96.33 200	94.83 329	90.82 225	96.38 131	97.20 260	96.59 90	97.49 149	98.57 66	77.67 313	99.38 214	92.95 212	99.62 64	98.80 194

test_prior3		95.91 171	95.39 185	97.46 132	97.79 225	94.26 144	93.33 285	98.42 171	94.21 189	94.02 279	96.25 261	93.64 176	99.34 224	91.90 221	98.96 210	98.79 195

test_prior				97.46 132	97.79 225	94.26 144		98.42 171					99.34 224			98.79 195

eth_miper_zixun_eth		94.89 211	94.93 200	94.75 264	95.99 308	86.12 299	91.35 319	98.49 162	93.40 210	97.12 168	97.25 204	86.87 276	99.35 222	95.08 138	98.82 230	98.78 197

cl_fuxian		95.20 199	95.32 186	94.83 261	96.19 301	86.43 296	91.83 313	98.35 183	93.47 209	97.36 159	97.26 203	88.69 257	99.28 240	95.41 118	99.36 149	98.78 197

MVS_111021_LR		96.82 130	96.55 140	97.62 114	98.27 164	95.34 104	93.81 270	98.33 184	94.59 177	96.56 202	96.63 243	96.61 74	98.73 301	94.80 148	99.34 157	98.78 197

agg_prior1		95.39 192	94.60 217	97.75 103	97.80 219	94.96 117	93.39 282	98.36 179	87.20 293	93.49 298	95.97 275	94.65 149	99.53 166	91.69 229	98.86 225	98.77 200

F-COLMAP		95.30 196	94.38 228	98.05 88	98.64 123	96.04 73	95.61 179	98.66 143	89.00 276	93.22 306	96.40 256	92.90 191	99.35 222	87.45 307	97.53 293	98.77 200

D2MVS		95.18 200	95.17 190	95.21 244	97.76 231	87.76 276	94.15 253	97.94 223	89.77 270	96.99 180	97.68 168	87.45 271	99.14 258	95.03 141	99.81 30	98.74 202

MVSFormer		96.14 162	96.36 150	95.49 235	97.68 237	87.81 274	98.67 12	99.02 50	96.50 93	94.48 268	96.15 265	86.90 274	99.92 4	98.73 8	99.13 191	98.74 202

jason		94.39 235	94.04 239	95.41 240	98.29 160	87.85 273	92.74 297	96.75 277	85.38 314	95.29 249	96.15 265	88.21 263	99.65 127	94.24 173	99.34 157	98.74 202
jason: jason.
cl-mvsnet1		94.73 217	94.64 213	95.01 251	95.86 310	87.00 288	91.33 320	98.08 214	93.34 213	97.10 170	97.34 197	84.02 290	99.31 231	95.15 132	99.55 89	98.72 205

旧先验1					97.80 219	93.87 156		97.75 235			97.04 217	93.57 178			98.68 241	98.72 205

cl-mvsnet_		94.73 217	94.64 213	95.01 251	95.85 311	87.00 288	91.33 320	98.08 214	93.34 213	97.10 170	97.33 198	84.01 291	99.30 234	95.14 133	99.56 83	98.71 207

mvs_anonymous		95.36 193	96.07 163	93.21 298	96.29 295	81.56 332	94.60 235	97.66 242	93.30 215	96.95 184	98.91 46	93.03 189	99.38 214	96.60 61	97.30 302	98.69 208

OMC-MVS		96.48 150	96.00 165	97.91 95	98.30 159	96.01 76	94.86 225	98.60 151	91.88 247	97.18 164	97.21 206	96.11 96	99.04 271	90.49 264	99.34 157	98.69 208

thisisatest0530		92.71 273	91.76 281	95.56 232	98.42 152	88.23 263	96.03 151	87.35 349	94.04 196	96.56 202	95.47 290	64.03 354	99.77 49	94.78 151	99.11 195	98.68 210

TAMVS		95.49 185	94.94 198	97.16 151	98.31 158	93.41 174	95.07 213	96.82 275	91.09 257	97.51 147	97.82 153	89.96 244	99.42 195	88.42 293	99.44 125	98.64 211

test_0402		97.84 65	97.97 42	97.47 130	99.19 67	94.07 149	96.71 119	98.73 123	98.66 24	98.56 58	98.41 78	96.84 65	99.69 109	94.82 147	99.81 30	98.64 211

MVP-Stereo		95.69 177	95.28 187	96.92 164	98.15 182	93.03 183	95.64 178	98.20 197	90.39 263	96.63 199	97.73 162	91.63 224	99.10 265	91.84 225	97.31 301	98.63 213
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
cl-mvsnet2		93.25 266	92.84 262	94.46 275	94.30 335	86.00 300	91.09 327	96.64 281	90.74 259	95.79 236	96.31 259	78.24 310	98.77 297	94.15 177	98.34 258	98.62 214

CANet_DTU		94.65 226	94.21 233	95.96 215	95.90 309	89.68 238	93.92 265	97.83 232	93.19 219	90.12 336	95.64 285	88.52 258	99.57 156	93.27 205	99.47 117	98.62 214

PM-MVS		97.36 102	97.10 109	98.14 80	98.91 98	96.77 49	96.20 142	98.63 149	93.82 201	98.54 59	98.33 84	93.98 168	99.05 270	95.99 86	99.45 124	98.61 216

CSCG		97.40 98	97.30 95	97.69 110	98.95 95	94.83 120	97.28 89	98.99 61	96.35 100	98.13 104	95.95 277	95.99 99	99.66 126	94.36 170	99.73 44	98.59 217

CLD-MVS		95.47 188	95.07 193	96.69 179	98.27 164	92.53 191	91.36 318	98.67 141	91.22 256	95.78 238	94.12 314	95.65 117	98.98 279	90.81 247	99.72 47	98.57 218
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UnsupCasMVSNet_bld		94.72 221	94.26 230	96.08 211	98.62 128	90.54 233	93.38 283	98.05 220	90.30 264	97.02 178	96.80 233	89.54 248	99.16 257	88.44 292	96.18 320	98.56 219

N_pmnet		95.18 200	94.23 231	98.06 85	97.85 207	96.55 58	92.49 301	91.63 331	89.34 272	98.09 109	97.41 186	90.33 237	99.06 269	91.58 230	99.31 168	98.56 219

CVMVSNet		92.33 280	92.79 263	90.95 322	97.26 269	75.84 350	95.29 198	92.33 326	81.86 329	96.27 217	98.19 106	81.44 297	98.46 322	94.23 174	98.29 261	98.55 221

LS3D		97.77 72	97.50 85	98.57 49	96.24 297	97.58 24	98.45 24	98.85 89	98.58 26	97.51 147	97.94 138	95.74 114	99.63 132	95.19 126	98.97 209	98.51 222

miper_ehance_all_eth		94.69 222	94.70 210	94.64 266	95.77 314	86.22 298	91.32 322	98.24 192	91.67 249	97.05 175	96.65 242	88.39 261	99.22 250	94.88 144	98.34 258	98.49 223

Effi-MVS+-dtu		96.81 131	96.09 161	98.99 13	96.90 284	98.69 2	96.42 127	98.09 212	95.86 126	95.15 252	95.54 288	94.26 161	99.81 32	94.06 180	98.51 254	98.47 224

USDC		94.56 230	94.57 222	94.55 273	97.78 229	86.43 296	92.75 295	98.65 148	85.96 303	96.91 187	97.93 140	90.82 232	98.74 300	90.71 254	99.59 75	98.47 224

pmmvs4		94.82 214	94.19 234	96.70 178	97.42 257	92.75 189	92.09 310	96.76 276	86.80 298	95.73 241	97.22 205	89.28 254	98.89 287	93.28 203	99.14 187	98.46 226

alignmvs		96.01 168	95.52 183	97.50 125	97.77 230	94.71 125	96.07 148	96.84 273	97.48 65	96.78 194	94.28 313	85.50 282	99.40 206	96.22 73	98.73 240	98.40 227

CDS-MVSNet		94.88 212	94.12 236	97.14 153	97.64 242	93.57 170	93.96 264	97.06 267	90.05 267	96.30 216	96.55 246	86.10 278	99.47 182	90.10 269	99.31 168	98.40 227
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
WTY-MVS		93.55 259	93.00 258	95.19 245	97.81 215	87.86 271	93.89 266	96.00 287	89.02 275	94.07 277	95.44 291	86.27 277	99.33 227	87.69 301	96.82 308	98.39 229

Effi-MVS+		96.19 160	96.01 164	96.71 177	97.43 256	92.19 201	96.12 146	99.10 29	95.45 143	93.33 305	94.71 303	97.23 43	99.56 157	93.21 207	97.54 292	98.37 230

MS-PatchMatch		94.83 213	94.91 202	94.57 272	96.81 286	87.10 287	94.23 248	97.34 257	88.74 280	97.14 166	97.11 211	91.94 218	98.23 334	92.99 210	97.92 273	98.37 230

TSAR-MVS + GP.		96.47 151	96.12 159	97.49 128	97.74 233	95.23 107	94.15 253	96.90 272	93.26 216	98.04 116	96.70 239	94.41 157	98.89 287	94.77 152	99.14 187	98.37 230

DELS-MVS		96.17 161	96.23 154	95.99 213	97.55 248	90.04 235	92.38 305	98.52 159	94.13 193	96.55 204	97.06 215	94.99 139	99.58 150	95.62 100	99.28 173	98.37 230
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
sss		94.22 238	93.72 246	95.74 224	97.71 235	89.95 237	93.84 267	96.98 269	88.38 284	93.75 287	95.74 281	87.94 264	98.89 287	91.02 241	98.10 267	98.37 230

GA-MVS		92.83 271	92.15 276	94.87 258	96.97 279	87.27 285	90.03 336	96.12 285	91.83 248	94.05 278	94.57 304	76.01 325	98.97 283	92.46 217	97.34 300	98.36 235

ITE_SJBPF				97.85 99	98.64 123	96.66 53		98.51 161	95.63 135	97.22 162	97.30 201	95.52 120	98.55 317	90.97 242	98.90 219	98.34 236

LCM-MVSNet-Re		97.33 103	97.33 94	97.32 144	98.13 186	93.79 161	96.99 105	99.65 2	96.74 86	99.47 14	98.93 44	96.91 59	99.84 26	90.11 268	99.06 204	98.32 237

BH-RMVSNet		94.56 230	94.44 227	94.91 254	97.57 245	87.44 281	93.78 271	96.26 283	93.69 205	96.41 209	96.50 251	92.10 213	99.00 275	85.96 315	97.71 283	98.31 238

MG-MVS		94.08 246	94.00 240	94.32 279	97.09 276	85.89 301	93.19 289	95.96 289	92.52 236	94.93 257	97.51 179	89.54 248	98.77 297	87.52 306	97.71 283	98.31 238

AUN-MVS		93.95 250	92.69 267	97.74 104	97.80 219	95.38 99	95.57 180	95.46 299	91.26 255	92.64 317	96.10 270	74.67 329	99.55 161	93.72 195	96.97 304	98.30 240

MVS_Test		96.27 156	96.79 129	94.73 265	96.94 282	86.63 293	96.18 143	98.33 184	94.94 164	96.07 226	98.28 93	95.25 131	99.26 243	97.21 46	97.90 275	98.30 240

TinyColmap		96.00 169	96.34 151	94.96 253	97.90 205	87.91 270	94.13 256	98.49 162	94.41 181	98.16 99	97.76 156	96.29 94	98.68 308	90.52 261	99.42 135	98.30 240

CMPMVS	73.10 23	92.74 272	91.39 284	96.77 174	93.57 345	94.67 129	94.21 250	97.67 240	80.36 338	93.61 293	96.60 244	82.85 294	97.35 344	84.86 325	98.78 233	98.29 243
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
lupinMVS		93.77 251	93.28 252	95.24 243	97.68 237	87.81 274	92.12 308	96.05 286	84.52 321	94.48 268	95.06 296	86.90 274	99.63 132	93.62 198	99.13 191	98.27 244

PAPM_NR		94.61 228	94.17 235	95.96 215	98.36 156	91.23 217	95.93 161	97.95 222	92.98 228	93.42 303	94.43 310	90.53 234	98.38 327	87.60 303	96.29 319	98.27 244

114514_t		93.96 248	93.22 255	96.19 207	99.06 87	90.97 222	95.99 155	98.94 71	73.88 352	93.43 302	96.93 223	92.38 208	99.37 217	89.09 283	99.28 173	98.25 246

原ACMM1				96.58 186	98.16 180	92.12 202		98.15 206	85.90 305	93.49 298	96.43 253	92.47 206	99.38 214	87.66 302	98.62 247	98.23 247

PLC	91.02 16	94.05 247	92.90 259	97.51 122	98.00 197	95.12 114	94.25 246	98.25 191	86.17 301	91.48 327	95.25 292	91.01 229	99.19 251	85.02 324	96.69 312	98.22 248
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPNet_dtu		91.39 293	90.75 296	93.31 294	90.48 358	82.61 328	94.80 228	92.88 320	93.39 211	81.74 355	94.90 301	81.36 298	99.11 263	88.28 295	98.87 223	98.21 249
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
1112_ss		94.12 243	93.42 250	96.23 204	98.59 133	90.85 223	94.24 247	98.85 89	85.49 309	92.97 309	94.94 298	86.01 279	99.64 130	91.78 226	97.92 273	98.20 250

Test_1112_low_res		93.53 260	92.86 260	95.54 233	98.60 131	88.86 253	92.75 295	98.69 136	82.66 328	92.65 316	96.92 225	84.75 287	99.56 157	90.94 243	97.76 279	98.19 251

canonicalmvs		97.23 110	97.21 104	97.30 145	97.65 241	94.39 136	97.84 56	99.05 41	97.42 67	96.68 196	93.85 316	97.63 27	99.33 227	96.29 72	98.47 255	98.18 252

miper_enhance_ethall		93.14 268	92.78 265	94.20 282	93.65 343	85.29 308	89.97 337	97.85 228	85.05 316	96.15 225	94.56 305	85.74 280	99.14 258	93.74 193	98.34 258	98.17 253

Fast-Effi-MVS+-dtu		96.44 152	96.12 159	97.39 140	97.18 273	94.39 136	95.46 182	98.73 123	96.03 115	94.72 259	94.92 300	96.28 95	99.69 109	93.81 191	97.98 271	98.09 254

ab-mvs		96.59 145	96.59 136	96.60 183	98.64 123	92.21 198	98.35 27	97.67 240	94.45 180	96.99 180	98.79 51	94.96 140	99.49 176	90.39 265	99.07 201	98.08 255

PAPR		92.22 281	91.27 287	95.07 249	95.73 316	88.81 254	91.97 311	97.87 227	85.80 306	90.91 329	92.73 328	91.16 227	98.33 331	79.48 340	95.76 326	98.08 255

test_yl		94.40 233	94.00 240	95.59 228	96.95 280	89.52 241	94.75 231	95.55 297	96.18 106	96.79 190	96.14 267	81.09 299	99.18 252	90.75 250	97.77 277	98.07 257

DCV-MVSNet		94.40 233	94.00 240	95.59 228	96.95 280	89.52 241	94.75 231	95.55 297	96.18 106	96.79 190	96.14 267	81.09 299	99.18 252	90.75 250	97.77 277	98.07 257

baseline1		93.14 268	92.64 269	94.62 268	97.34 264	87.20 286	96.67 121	93.02 318	94.71 172	96.51 205	95.83 280	81.64 296	98.60 313	90.00 271	88.06 348	98.07 257

MIMVSNet		93.42 261	92.86 260	95.10 248	98.17 178	88.19 264	98.13 42	93.69 310	92.07 242	95.04 254	98.21 105	80.95 301	99.03 274	81.42 337	98.06 269	98.07 257

GSMVS																98.06 261

sam_mvs1												77.80 312				98.06 261

SCA		93.38 263	93.52 249	92.96 305	96.24 297	81.40 333	93.24 287	94.00 309	91.58 252	94.57 263	96.97 220	87.94 264	99.42 195	89.47 278	97.66 288	98.06 261

MSLP-MVS++		96.42 154	96.71 131	95.57 230	97.82 214	90.56 232	95.71 169	98.84 93	94.72 171	96.71 195	97.39 191	94.91 142	98.10 338	95.28 121	99.02 206	98.05 264

ADS-MVSNet2		91.47 292	90.51 300	94.36 278	95.51 319	85.63 302	95.05 216	95.70 293	83.46 325	92.69 314	96.84 228	79.15 307	99.41 204	85.66 319	90.52 343	98.04 265

ADS-MVSNet		90.95 298	90.26 302	93.04 301	95.51 319	82.37 330	95.05 216	93.41 315	83.46 325	92.69 314	96.84 228	79.15 307	98.70 304	85.66 319	90.52 343	98.04 265

PVSNet_Blended		93.96 248	93.65 247	94.91 254	97.79 225	87.40 282	91.43 317	98.68 138	84.50 322	94.51 266	94.48 309	93.04 187	99.30 234	89.77 274	98.61 248	98.02 267

PatchmatchNet		91.98 286	91.87 278	92.30 314	94.60 332	79.71 338	95.12 207	93.59 314	89.52 271	93.61 293	97.02 218	77.94 311	99.18 252	90.84 246	94.57 335	98.01 268
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PVSNet	86.72 19	91.10 295	90.97 292	91.49 318	97.56 247	78.04 342	87.17 346	94.60 305	84.65 320	92.34 321	92.20 333	87.37 272	98.47 321	85.17 323	97.69 285	97.96 269

无先验							93.20 288	97.91 224	80.78 335				99.40 206	87.71 299		97.94 270

MVS_0304		95.50 184	95.05 196	96.84 170	96.28 296	93.12 181	97.00 104	96.16 284	95.03 161	89.22 341	97.70 165	90.16 243	99.48 179	94.51 161	99.34 157	97.93 271

EIA-MVS		96.04 166	95.77 175	96.85 169	97.80 219	92.98 184	96.12 146	99.16 18	94.65 173	93.77 286	91.69 339	95.68 115	99.67 120	94.18 175	98.85 227	97.91 272

tpm		91.08 296	90.85 294	91.75 317	95.33 324	78.09 341	95.03 218	91.27 334	88.75 279	93.53 297	97.40 187	71.24 342	99.30 234	91.25 237	93.87 336	97.87 273

Patchmatch-RL test		94.66 225	94.49 223	95.19 245	98.54 138	88.91 251	92.57 299	98.74 121	91.46 253	98.32 83	97.75 159	77.31 318	98.81 294	96.06 78	99.61 70	97.85 274

LF4IMVS		96.07 164	95.63 179	97.36 142	98.19 173	95.55 91	95.44 183	98.82 108	92.29 241	95.70 242	96.55 246	92.63 199	98.69 305	91.75 228	99.33 164	97.85 274

ET-MVSNet_ETH3D		91.12 294	89.67 306	95.47 236	96.41 293	89.15 249	91.54 316	90.23 343	89.07 274	86.78 351	92.84 325	69.39 348	99.44 192	94.16 176	96.61 314	97.82 276

MDTV_nov1_ep13_2view						57.28 360	94.89 223		80.59 336	94.02 279		78.66 309		85.50 321		97.82 276

Patchmatch-test		93.60 258	93.25 254	94.63 267	96.14 306	87.47 280	96.04 150	94.50 306	93.57 206	96.47 206	96.97 220	76.50 321	98.61 311	90.67 256	98.41 257	97.81 278

Fast-Effi-MVS+		95.49 185	95.07 193	96.75 175	97.67 240	92.82 186	94.22 249	98.60 151	91.61 250	93.42 303	92.90 324	96.73 69	99.70 101	92.60 213	97.89 276	97.74 279

DPM-MVS		93.68 255	92.77 266	96.42 196	97.91 203	92.54 190	91.17 325	97.47 255	84.99 318	93.08 308	94.74 302	89.90 245	99.00 275	87.54 305	98.09 268	97.72 280

baseline2		89.65 309	88.44 316	93.25 296	95.62 317	82.71 327	93.82 268	85.94 352	88.89 278	87.35 349	92.54 330	71.23 343	99.33 227	86.01 314	94.60 334	97.72 280

1121		94.26 236	93.26 253	97.27 146	98.26 166	94.73 123	95.86 163	97.71 238	77.96 346	94.53 265	96.71 238	91.93 219	99.40 206	87.71 299	98.64 246	97.69 282

test222					98.17 178	93.24 178	92.74 297	97.61 250	75.17 350	94.65 262	96.69 240	90.96 231			98.66 244	97.66 283

TAPA-MVS	93.32 12	94.93 209	94.23 231	97.04 159	98.18 176	94.51 132	95.22 204	98.73 123	81.22 334	96.25 219	95.95 277	93.80 173	98.98 279	89.89 272	98.87 223	97.62 284
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
新几何1				97.25 149	98.29 160	94.70 128		97.73 236	77.98 345	94.83 258	96.67 241	92.08 214	99.45 189	88.17 297	98.65 245	97.61 285

MSDG		95.33 194	95.13 191	95.94 219	97.40 258	91.85 209	91.02 328	98.37 178	95.30 149	96.31 215	95.99 272	94.51 155	98.38 327	89.59 276	97.65 289	97.60 286

testdata				95.70 227	98.16 180	90.58 230		97.72 237	80.38 337	95.62 243	97.02 218	92.06 215	98.98 279	89.06 285	98.52 252	97.54 287

DSMNet-mixed		92.19 282	91.83 279	93.25 296	96.18 302	83.68 326	96.27 136	93.68 312	76.97 349	92.54 320	99.18 28	89.20 256	98.55 317	83.88 330	98.60 250	97.51 288

thisisatest0515		90.43 300	89.18 312	94.17 284	97.07 277	85.44 305	89.75 342	87.58 348	88.28 285	93.69 290	91.72 338	65.27 352	99.58 150	90.59 258	98.67 242	97.50 289

PMMVS		92.39 277	91.08 289	96.30 203	93.12 349	92.81 187	90.58 332	95.96 289	79.17 342	91.85 326	92.27 332	90.29 241	98.66 310	89.85 273	96.68 313	97.43 290

DP-MVS Recon		95.55 183	95.13 191	96.80 172	98.51 141	93.99 153	94.60 235	98.69 136	90.20 265	95.78 238	96.21 264	92.73 195	98.98 279	90.58 259	98.86 225	97.42 291

thres600view7		92.03 285	91.43 283	93.82 285	98.19 173	84.61 318	96.27 136	90.39 340	96.81 84	96.37 211	93.11 319	73.44 337	99.49 176	80.32 339	97.95 272	97.36 292

thres400		91.68 290	91.00 290	93.71 287	98.02 191	84.35 321	95.70 170	90.79 337	96.26 102	95.90 234	92.13 334	73.62 335	99.42 195	78.85 343	97.74 280	97.36 292

OpenMVS	94.22 8	95.48 187	95.20 188	96.32 201	97.16 274	91.96 207	97.74 62	98.84 93	87.26 292	94.36 270	98.01 129	93.95 169	99.67 120	90.70 255	98.75 236	97.35 294

CS-MVS		95.86 174	95.59 181	96.69 179	97.85 207	93.14 180	96.42 127	99.25 10	94.17 192	93.56 296	90.76 347	96.05 98	99.72 77	93.28 203	98.91 218	97.21 295

test0.0.03 1		90.11 302	89.21 309	92.83 307	93.89 341	86.87 291	91.74 314	88.74 347	92.02 243	94.71 260	91.14 343	73.92 332	94.48 352	83.75 333	92.94 338	97.16 296

BH-untuned		94.69 222	94.75 209	94.52 274	97.95 202	87.53 279	94.07 258	97.01 268	93.99 197	97.10 170	95.65 284	92.65 198	98.95 284	87.60 303	96.74 311	97.09 297

mvs-test1		96.20 159	95.50 184	98.32 65	96.90 284	98.16 4	95.07 213	98.09 212	95.86 126	93.63 291	94.32 312	94.26 161	99.71 92	94.06 180	97.27 303	97.07 298

new_pmnet		92.34 279	91.69 282	94.32 279	96.23 299	89.16 248	92.27 306	92.88 320	84.39 324	95.29 249	96.35 258	85.66 281	96.74 349	84.53 327	97.56 291	97.05 299

tpmrst		90.31 301	90.61 299	89.41 329	94.06 340	72.37 356	95.06 215	93.69 310	88.01 287	92.32 322	96.86 226	77.45 315	98.82 292	91.04 240	87.01 350	97.04 300

EPMVS		89.26 311	88.55 315	91.39 319	92.36 354	79.11 339	95.65 176	79.86 355	88.60 281	93.12 307	96.53 248	70.73 346	98.10 338	90.75 250	89.32 347	96.98 301

Gipuma		98.07 41	98.31 30	97.36 142	99.76 5	96.28 67	98.51 20	99.10 29	98.76 22	96.79 190	99.34 18	96.61 74	98.82 292	96.38 70	99.50 107	96.98 301
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test-LLR		89.97 306	89.90 304	90.16 326	94.24 337	74.98 351	89.89 338	89.06 345	92.02 243	89.97 337	90.77 345	73.92 332	98.57 314	91.88 223	97.36 298	96.92 303

test-mter		87.92 319	87.17 320	90.16 326	94.24 337	74.98 351	89.89 338	89.06 345	86.44 300	89.97 337	90.77 345	54.96 362	98.57 314	91.88 223	97.36 298	96.92 303

PCF-MVS	89.43 18	92.12 284	90.64 298	96.57 188	97.80 219	93.48 173	89.88 341	98.45 165	74.46 351	96.04 227	95.68 283	90.71 233	99.31 231	73.73 348	99.01 208	96.91 305
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CostFormer		89.75 308	89.25 307	91.26 321	94.69 331	78.00 343	95.32 195	91.98 328	81.50 332	90.55 332	96.96 222	71.06 344	98.89 287	88.59 291	92.63 340	96.87 306

dp		88.08 317	88.05 317	88.16 335	92.85 351	68.81 358	94.17 251	92.88 320	85.47 310	91.38 328	96.14 267	68.87 349	98.81 294	86.88 310	83.80 353	96.87 306

ETV-MVS		96.13 163	95.90 171	96.82 171	97.76 231	93.89 155	95.40 188	98.95 70	95.87 125	95.58 245	91.00 344	96.36 91	99.72 77	93.36 200	98.83 229	96.85 308

cascas		91.89 287	91.35 285	93.51 291	94.27 336	85.60 303	88.86 344	98.61 150	79.32 341	92.16 323	91.44 340	89.22 255	98.12 337	90.80 248	97.47 297	96.82 309

CR-MVSNet		93.29 265	92.79 263	94.78 263	95.44 321	88.15 265	96.18 143	97.20 260	84.94 319	94.10 275	98.57 66	77.67 313	99.39 211	95.17 128	95.81 322	96.81 310

RPMNet		94.68 224	94.60 217	94.90 256	95.44 321	88.15 265	96.18 143	98.86 85	97.43 66	94.10 275	98.49 73	79.40 305	99.76 53	95.69 94	95.81 322	96.81 310

PatchMatch-RL		94.61 228	93.81 245	97.02 161	98.19 173	95.72 82	93.66 273	97.23 259	88.17 286	94.94 256	95.62 286	91.43 225	98.57 314	87.36 308	97.68 286	96.76 312

MAR-MVS		94.21 240	93.03 257	97.76 102	96.94 282	97.44 33	96.97 106	97.15 263	87.89 290	92.00 324	92.73 328	92.14 211	99.12 260	83.92 329	97.51 294	96.73 313
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
DWT-MVSNet_test		87.92 319	86.77 323	91.39 319	93.18 346	78.62 340	95.10 208	91.42 332	85.58 308	88.00 345	88.73 350	60.60 356	98.90 285	90.60 257	87.70 349	96.65 314

TESTMET0.1,1		87.20 322	86.57 324	89.07 330	93.62 344	72.84 355	89.89 338	87.01 351	85.46 311	89.12 342	90.20 348	56.00 361	97.72 342	90.91 244	96.92 305	96.64 315

CNLPA		95.04 206	94.47 224	96.75 175	97.81 215	95.25 106	94.12 257	97.89 226	94.41 181	94.57 263	95.69 282	90.30 240	98.35 330	86.72 312	98.76 235	96.64 315

IB-MVS	85.98 20	88.63 313	86.95 322	93.68 288	95.12 326	84.82 317	90.85 329	90.17 344	87.55 291	88.48 344	91.34 341	58.01 357	99.59 148	87.24 309	93.80 337	96.63 317
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
tpmvs		90.79 299	90.87 293	90.57 325	92.75 353	76.30 348	95.79 167	93.64 313	91.04 258	91.91 325	96.26 260	77.19 319	98.86 291	89.38 280	89.85 346	96.56 318

CHOSEN 280x420		89.98 305	89.19 311	92.37 313	95.60 318	81.13 335	86.22 348	97.09 266	81.44 333	87.44 348	93.15 318	73.99 330	99.47 182	88.69 289	99.07 201	96.52 319

HY-MVS	91.43 15	92.58 274	91.81 280	94.90 256	96.49 291	88.87 252	97.31 87	94.62 304	85.92 304	90.50 333	96.84 228	85.05 284	99.40 206	83.77 332	95.78 325	96.43 320

PatchT		93.75 252	93.57 248	94.29 281	95.05 327	87.32 284	96.05 149	92.98 319	97.54 63	94.25 271	98.72 56	75.79 326	99.24 246	95.92 89	95.81 322	96.32 321

tpm2		88.47 314	87.69 318	90.79 323	94.98 328	77.34 346	95.09 210	91.83 329	77.51 348	89.40 339	96.41 254	67.83 350	98.73 301	83.58 334	92.60 341	96.29 322

AdaColmap		95.11 203	94.62 216	96.58 186	97.33 266	94.45 135	94.92 222	98.08 214	93.15 224	93.98 282	95.53 289	94.34 159	99.10 265	85.69 318	98.61 248	96.20 323

pmmvs3		90.00 304	88.90 313	93.32 293	94.20 339	85.34 306	91.25 323	92.56 325	78.59 343	93.82 283	95.17 293	67.36 351	98.69 305	89.08 284	98.03 270	95.92 324

thres100view900		91.76 289	91.26 288	93.26 295	98.21 171	84.50 319	96.39 129	90.39 340	96.87 82	96.33 212	93.08 321	73.44 337	99.42 195	78.85 343	97.74 280	95.85 325

tfpn200view9		91.55 291	91.00 290	93.21 298	98.02 191	84.35 321	95.70 170	90.79 337	96.26 102	95.90 234	92.13 334	73.62 335	99.42 195	78.85 343	97.74 280	95.85 325

OpenMVS_ROB	91.80 14	93.64 257	93.05 256	95.42 238	97.31 268	91.21 218	95.08 212	96.68 280	81.56 331	96.88 189	96.41 254	90.44 236	99.25 245	85.39 322	97.67 287	95.80 327

PAPM		87.64 321	85.84 326	93.04 301	96.54 289	84.99 314	88.42 345	95.57 296	79.52 340	83.82 352	93.05 323	80.57 302	98.41 324	62.29 354	92.79 339	95.71 328

xiu_mvs_v1_base_debu		95.62 180	95.96 168	94.60 269	98.01 193	88.42 259	93.99 261	98.21 194	92.98 228	95.91 231	94.53 306	96.39 88	99.72 77	95.43 115	98.19 263	95.64 329

xiu_mvs_v1_base		95.62 180	95.96 168	94.60 269	98.01 193	88.42 259	93.99 261	98.21 194	92.98 228	95.91 231	94.53 306	96.39 88	99.72 77	95.43 115	98.19 263	95.64 329

xiu_mvs_v1_base_debi		95.62 180	95.96 168	94.60 269	98.01 193	88.42 259	93.99 261	98.21 194	92.98 228	95.91 231	94.53 306	96.39 88	99.72 77	95.43 115	98.19 263	95.64 329

tpm cat1		88.01 318	87.33 319	90.05 328	94.48 333	76.28 349	94.47 240	94.35 308	73.84 353	89.26 340	95.61 287	73.64 334	98.30 332	84.13 328	86.20 351	95.57 332

JIA-IIPM		91.79 288	90.69 297	95.11 247	93.80 342	90.98 221	94.16 252	91.78 330	96.38 97	90.30 335	99.30 19	72.02 341	98.90 285	88.28 295	90.17 345	95.45 333

TR-MVS		92.54 275	92.20 275	93.57 290	96.49 291	86.66 292	93.51 278	94.73 303	89.96 268	94.95 255	93.87 315	90.24 242	98.61 311	81.18 338	94.88 330	95.45 333

thres200		91.00 297	90.42 301	92.77 308	97.47 254	83.98 324	94.01 260	91.18 335	95.12 157	95.44 246	91.21 342	73.93 331	99.31 231	77.76 346	97.63 290	95.01 335

1314		92.38 278	92.30 274	92.64 310	95.42 323	85.15 311	95.86 163	96.97 270	85.40 313	90.62 330	93.06 322	91.12 228	97.80 341	86.74 311	95.49 329	94.97 336

BH-w/o		92.14 283	91.94 277	92.73 309	97.13 275	85.30 307	92.46 302	95.64 294	89.33 273	94.21 272	92.74 327	89.60 247	98.24 333	81.68 336	94.66 332	94.66 337

xiu_mvs_v2_base		94.22 238	94.63 215	92.99 304	97.32 267	84.84 316	92.12 308	97.84 230	91.96 245	94.17 273	93.43 317	96.07 97	99.71 92	91.27 235	97.48 295	94.42 338

PS-MVSNAJ		94.10 244	94.47 224	93.00 303	97.35 260	84.88 315	91.86 312	97.84 230	91.96 245	94.17 273	92.50 331	95.82 106	99.71 92	91.27 235	97.48 295	94.40 339

gg-mvs-nofinetune		88.28 316	86.96 321	92.23 316	92.84 352	84.44 320	98.19 39	74.60 357	99.08 11	87.01 350	99.47 9	56.93 359	98.23 334	78.91 342	95.61 327	94.01 340

API-MVS		95.09 205	95.01 197	95.31 241	96.61 288	94.02 151	96.83 109	97.18 262	95.60 137	95.79 236	94.33 311	94.54 154	98.37 329	85.70 317	98.52 252	93.52 341

PVSNet_0	81.89 21	84.49 324	83.21 327	88.34 333	95.76 315	74.97 353	83.49 350	92.70 324	78.47 344	87.94 346	86.90 352	83.38 293	96.63 350	73.44 349	66.86 355	93.40 342

FPMVS		89.92 307	88.63 314	93.82 285	98.37 155	96.94 45	91.58 315	93.34 316	88.00 288	90.32 334	97.10 212	70.87 345	91.13 354	71.91 351	96.16 321	93.39 343

PMVS	89.60 17	96.71 139	96.97 117	95.95 217	99.51 23	97.81 16	97.42 84	97.49 252	97.93 43	95.95 230	98.58 65	96.88 62	96.91 346	89.59 276	99.36 149	93.12 344
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVS		90.02 303	89.20 310	92.47 311	94.71 330	86.90 290	95.86 163	96.74 278	64.72 354	90.62 330	92.77 326	92.54 203	98.39 326	79.30 341	95.56 328	92.12 345

MVE	73.61 22	86.48 323	85.92 325	88.18 334	96.23 299	85.28 309	81.78 353	75.79 356	86.01 302	82.53 354	91.88 336	92.74 194	87.47 355	71.42 352	94.86 331	91.78 346
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		89.52 310	89.78 305	88.73 331	93.14 348	77.61 344	83.26 351	92.02 327	94.82 168	93.71 288	93.11 319	75.31 327	96.81 347	85.81 316	96.81 309	91.77 347

EMVS		89.06 312	89.22 308	88.61 332	93.00 350	77.34 346	82.91 352	90.92 336	94.64 174	92.63 318	91.81 337	76.30 323	97.02 345	83.83 331	96.90 306	91.48 348

GG-mvs-BLEND				90.60 324	91.00 356	84.21 323	98.23 33	72.63 360		82.76 353	84.11 353	56.14 360	96.79 348	72.20 350	92.09 342	90.78 349

MVS-HIRNet		88.40 315	90.20 303	82.99 337	97.01 278	60.04 359	93.11 290	85.61 353	84.45 323	88.72 343	99.09 34	84.72 288	98.23 334	82.52 335	96.59 315	90.69 350

DeepMVS_CX				77.17 338	90.94 357	85.28 309		74.08 359	52.51 355	80.87 356	88.03 351	75.25 328	70.63 356	59.23 355	84.94 352	75.62 351

wuyk23d		93.25 266	95.20 188	87.40 336	96.07 307	95.38 99	97.04 102	94.97 301	95.33 147	99.70 6	98.11 114	98.14 14	91.94 353	77.76 346	99.68 56	74.89 352

tmp_tt		57.23 325	62.50 328	41.44 339	34.77 360	49.21 361	83.93 349	60.22 361	15.31 356	71.11 357	79.37 354	70.09 347	44.86 357	64.76 353	82.93 354	30.25 353

test123		12.59 327	15.49 330	3.87 340	6.07 361	2.55 362	90.75 330	2.59 363	2.52 357	5.20 359	13.02 357	4.96 363	1.85 359	5.20 356	9.09 356	7.23 354

testmvs		12.33 328	15.23 331	3.64 341	5.77 362	2.23 363	88.99 343	3.62 362	2.30 358	5.29 358	13.09 356	4.52 364	1.95 358	5.16 357	8.32 357	6.75 355

uanet_test		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

cdsmvs_eth3d_5k		24.22 326	32.30 329	0.00 342	0.00 363	0.00 364	0.00 354	98.10 211	0.00 359	0.00 360	95.06 296	97.54 29	0.00 360	0.00 358	0.00 358	0.00 356

pcd_1.5k_mvsjas		7.98 329	10.65 332	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	95.82 106	0.00 360	0.00 358	0.00 358	0.00 356

sosnet-low-res		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

sosnet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

uncertanet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

Regformer		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

ab-mvs-re		7.91 330	10.55 333	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	94.94 298	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

uanet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

ZD-MVS					98.43 151	95.94 77		98.56 155	90.72 260	96.66 197	97.07 214	95.02 138	99.74 67	91.08 239	98.93 216

test_241102_ONE					99.22 57	95.35 102		98.83 100	96.04 113	99.08 31	98.13 110	97.87 21	99.33 227

9.14			96.69 132		98.53 139		96.02 152	98.98 64	93.23 217	97.18 164	97.46 183	96.47 84	99.62 140	92.99 210	99.32 166

save fliter					98.48 146	94.71 125	94.53 238	98.41 173	95.02 162

test0726					99.24 52	95.51 94	96.89 107	98.89 76	95.92 121	98.64 51	98.31 86	97.06 50

test_part2					99.03 91	96.07 72				98.08 111

sam_mvs												77.38 316

MTGPA								98.73 123

test_post1							94.98 220				10.37 359	76.21 324	99.04 271	89.47 278

test_post											10.87 358	76.83 320	99.07 268

patchmatchnet-post											96.84 228	77.36 317	99.42 195

MTMP							96.55 122	74.60 357

gm-plane-assit					91.79 355	71.40 357			81.67 330		90.11 349		98.99 277	84.86 325

TEST9					97.84 212	95.23 107	93.62 274	98.39 175	86.81 297	93.78 284	95.99 272	94.68 147	99.52 170

test_8					97.81 215	95.07 115	93.54 277	98.38 177	87.04 295	93.71 288	95.96 276	94.58 152	99.52 170

agg_prior					97.80 219	94.96 117		98.36 179		93.49 298			99.53 166

test_prior4						95.38 99	93.61 276

test_prior2							93.33 285		94.21 189	94.02 279	96.25 261	93.64 176		91.90 221	98.96 210

旧先验2							93.35 284		77.95 347	95.77 240			98.67 309	90.74 253

新几何2							93.43 279

原ACMM2							92.82 293

testdata2													99.46 185	87.84 298

segment_acmp												95.34 127

testdata1							92.77 294		93.78 202

plane_prior7					98.70 119	94.67 129

plane_prior6					98.38 154	94.37 138						91.91 221

plane_prior4											96.77 234

plane_prior3						94.51 132			95.29 150	96.16 223

plane_prior2							96.50 124		96.36 98

plane_prior1					98.49 144

plane_prior						94.29 140	95.42 185		94.31 186						98.93 216

n2								0.00 364

nn								0.00 364

door-mid								98.17 203

test11								98.08 214

door								97.81 233

HQP5-MVS						92.47 192

HQP-NCC					97.85 207		94.26 243		93.18 220	92.86 311

ACMP_Plane					97.85 207		94.26 243		93.18 220	92.86 311

BP-MVS														90.51 262

HQP3-MVS								98.43 168							98.74 237

HQP2-MVS												90.33 237

NP-MVS					98.14 183	93.72 164					95.08 294

MDTV_nov1_ep13			91.28 286		94.31 334	73.51 354	94.80 228	93.16 317	86.75 299	93.45 301	97.40 187	76.37 322	98.55 317	88.85 286	96.43 316

ACMMP++_ref															99.52 100

ACMMP++															99.55 89

Test By Simon												94.51 155

High-resolution multi-view benchmark