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		99.43 1	99.49 1	99.24 1	99.95 1	98.13 1	99.37 1	99.57 1	99.82 1	99.86 1	99.85 1	99.52 1	99.73 1	97.58 1	99.94 1	99.85 1

DTE-MVSNet		96.74 17	97.43 4	94.67 98	99.13 4	84.68 157	96.51 30	97.94 56	98.14 2	98.67 12	98.32 35	95.04 37	99.69 2	93.27 64	99.82 9	99.62 10

PS-CasMVS		96.69 19	97.43 4	94.49 111	99.13 4	84.09 165	96.61 25	97.97 50	97.91 4	98.64 13	98.13 40	95.24 30	99.65 3	93.39 60	99.84 5	99.72 2

PEN-MVS		96.69 19	97.39 7	94.61 100	99.16 2	84.50 158	96.54 29	98.05 38	98.06 3	98.64 13	98.25 38	95.01 40	99.65 3	92.95 77	99.83 7	99.68 4

K. test v3		93.37 139	93.27 147	93.66 136	98.05 72	82.62 181	94.35 113	86.62 316	96.05 27	97.51 42	98.85 12	76.59 281	99.65 3	93.21 67	98.20 180	98.73 102

CP-MVSNet		96.19 45	96.80 19	94.38 117	98.99 12	83.82 167	96.31 41	97.53 89	97.60 5	98.34 22	97.52 69	91.98 94	99.63 6	93.08 75	99.81 10	99.70 3

WR-MVS_H		96.60 24	97.05 15	95.24 82	99.02 10	86.44 131	96.78 22	98.08 33	97.42 7	98.48 18	97.86 56	91.76 98	99.63 6	94.23 37	99.84 5	99.66 6

PS-MVSNAJss		96.01 50	96.04 48	95.89 59	98.82 21	88.51 99	95.57 64	97.88 58	88.72 177	98.81 6	98.86 10	90.77 120	99.60 8	95.43 14	99.53 43	99.57 14

MVSFormer		92.18 180	92.23 170	92.04 205	94.74 257	80.06 215	97.15 12	97.37 102	88.98 165	88.83 288	92.79 264	77.02 276	99.60 8	96.41 6	96.75 248	96.46 227

test_djsdf		96.62 22	96.49 28	97.01 30	98.55 38	91.77 55	97.15 12	97.37 102	88.98 165	98.26 23	98.86 10	93.35 66	99.60 8	96.41 6	99.45 52	99.66 6

SixPastTwentyTwo		94.91 87	95.21 85	93.98 126	98.52 42	83.19 175	95.93 52	94.84 222	94.86 35	98.49 17	98.74 16	81.45 246	99.60 8	94.69 25	99.39 63	99.15 48

mvs_tets		96.83 9	96.71 21	97.17 25	98.83 20	92.51 44	96.58 27	97.61 80	87.57 204	98.80 7	98.90 9	96.50 10	99.59 12	96.15 9	99.47 48	99.40 31

UA-Net		97.35 4	97.24 12	97.69 5	98.22 61	93.87 26	98.42 4	98.19 25	96.95 12	95.46 128	99.23 3	93.45 59	99.57 13	95.34 17	99.89 4	99.63 9

OurMVSNet-221017-0		96.80 13	96.75 20	96.96 33	99.03 9	91.85 53	97.98 5	98.01 44	94.15 45	98.93 3	99.07 4	88.07 174	99.57 13	95.86 11	99.69 15	99.46 25

EPP-MVSNet		93.91 122	93.68 134	94.59 105	98.08 71	85.55 150	97.44 9	94.03 241	94.22 44	94.94 149	96.19 156	82.07 240	99.57 13	87.28 187	98.89 108	98.65 105

jajsoiax		96.59 26	96.42 29	97.12 27	98.76 24	92.49 45	96.44 35	97.42 98	86.96 214	98.71 10	98.72 17	95.36 25	99.56 16	95.92 10	99.45 52	99.32 37

v7n		96.82 10	97.31 9	95.33 79	98.54 39	86.81 125	96.83 19	98.07 36	96.59 18	98.46 19	98.43 32	92.91 76	99.52 17	96.25 8	99.76 12	99.65 8

ESAPD		95.89 51	95.88 56	95.92 58	97.93 81	89.83 74	93.46 137	98.30 15	92.37 81	97.75 36	96.95 97	95.14 33	99.51 18	91.74 109	99.28 75	98.41 119

HSP-MVS		95.18 77	94.49 104	97.23 24	98.67 26	94.05 18	96.41 37	97.00 133	91.26 119	95.12 140	95.15 194	86.60 207	99.50 19	93.43 59	96.81 245	98.13 135

anonymousdsp		96.74 17	96.42 29	97.68 7	98.00 76	94.03 21	96.97 16	97.61 80	87.68 203	98.45 21	98.77 15	94.20 52	99.50 19	96.70 5	99.40 61	99.53 16

APDe-MVS		96.46 32	96.64 23	95.93 56	97.68 96	89.38 81	96.90 18	98.41 10	92.52 78	97.43 46	97.92 51	95.11 35	99.50 19	94.45 30	99.30 70	98.92 83

v52		96.93 7	97.29 10	95.86 60	98.12 67	88.48 100	97.69 6	97.74 70	94.90 34	98.55 15	98.72 17	93.39 63	99.49 22	96.92 2	99.62 29	99.61 11

V4		96.93 7	97.29 10	95.86 60	98.11 68	88.47 101	97.69 6	97.74 70	94.91 32	98.55 15	98.72 17	93.37 64	99.49 22	96.92 2	99.62 29	99.61 11

PGM-MVS		96.32 40	95.94 52	97.43 15	98.59 34	93.84 28	95.33 71	98.30 15	91.40 117	95.76 116	96.87 106	95.26 29	99.45 24	92.77 79	99.21 82	99.00 68

TranMVSNet+NR-MVSNet		96.07 49	96.26 35	95.50 74	98.26 59	87.69 113	93.75 131	97.86 59	95.96 29	97.48 43	97.14 90	95.33 26	99.44 25	90.79 123	99.76 12	99.38 32

Vis-MVSNet		95.50 62	95.48 71	95.56 73	98.11 68	89.40 80	95.35 70	98.22 24	92.36 82	94.11 171	98.07 41	92.02 91	99.44 25	93.38 61	97.67 211	97.85 156
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GST-MVS		96.24 43	95.99 51	97.00 31	98.65 27	92.71 43	95.69 61	98.01 44	92.08 96	95.74 117	96.28 145	95.22 31	99.42 27	93.17 70	99.06 93	98.88 85

MP-MVS		96.14 46	95.68 66	97.51 10	98.81 22	94.06 16	96.10 47	97.78 69	92.73 71	93.48 186	96.72 116	94.23 51	99.42 27	91.99 102	99.29 72	99.05 63
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		96.46 32	96.05 47	97.69 5	98.62 29	94.65 9	96.45 33	97.74 70	92.59 77	95.47 126	96.68 118	94.50 48	99.42 27	93.10 73	99.26 76	98.99 70

HPM-MVS		96.81 12	96.62 24	97.36 20	98.89 17	93.53 34	97.51 8	98.44 7	92.35 84	95.95 105	96.41 133	96.71 8	99.42 27	93.99 42	99.36 65	99.13 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		96.44 35	96.08 45	97.54 9	98.29 56	94.62 10	96.80 20	98.08 33	92.67 74	95.08 145	96.39 138	94.77 44	99.42 27	93.17 70	99.44 54	98.58 113

region2R		96.41 36	96.09 44	97.38 19	98.62 29	93.81 31	96.32 40	97.96 51	92.26 87	95.28 134	96.57 123	95.02 39	99.41 32	93.63 49	99.11 91	98.94 78

ACMMPR		96.46 32	96.14 40	97.41 17	98.60 32	93.82 29	96.30 43	97.96 51	92.35 84	95.57 124	96.61 121	94.93 43	99.41 32	93.78 45	99.15 87	99.00 68

UniMVSNet_NR-MVSNet		95.35 68	95.21 85	95.76 65	97.69 95	88.59 95	92.26 186	97.84 62	94.91 32	96.80 66	95.78 173	90.42 130	99.41 32	91.60 114	99.58 39	99.29 39

DU-MVS		95.28 73	95.12 89	95.75 66	97.75 87	88.59 95	92.58 165	97.81 64	93.99 47	96.80 66	95.90 165	90.10 138	99.41 32	91.60 114	99.58 39	99.26 40

TSAR-MVS + MP.		94.96 85	94.75 95	95.57 72	98.86 19	88.69 91	96.37 38	96.81 151	85.23 233	94.75 154	97.12 92	91.85 96	99.40 36	93.45 57	98.33 163	98.62 109

FC-MVSNet-test		95.32 69	95.88 56	93.62 137	98.49 46	81.77 189	95.90 54	98.32 12	93.93 50	97.53 41	97.56 66	88.48 159	99.40 36	92.91 78	99.83 7	99.68 4

abl_6		97.31 5	97.12 14	97.86 3	98.54 39	95.32 8	96.61 25	98.35 11	95.81 30	97.55 40	97.44 75	96.51 9	99.40 36	94.06 41	99.23 80	98.85 90

ACMMP		96.61 23	96.34 32	97.43 15	98.61 31	93.88 25	96.95 17	98.18 26	92.26 87	96.33 82	96.84 109	95.10 36	99.40 36	93.47 56	99.33 68	99.02 67
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
tttt0517		89.81 223	88.90 229	92.55 188	97.00 124	79.73 233	95.03 83	83.65 345	89.88 152	95.30 132	94.79 211	53.64 358	99.39 40	91.99 102	98.79 126	98.54 114

MP-MVS-pluss		96.08 48	95.92 54	96.57 42	99.06 8	91.21 60	93.25 147	98.32 12	87.89 198	96.86 64	97.38 78	95.55 20	99.39 40	95.47 13	99.47 48	99.11 52
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
XVS		96.49 29	96.18 38	97.44 13	98.56 35	93.99 22	96.50 31	97.95 53	94.58 37	94.38 163	96.49 125	94.56 46	99.39 40	93.57 50	99.05 96	98.93 79

X-MVStestdata		90.70 204	88.45 234	97.44 13	98.56 35	93.99 22	96.50 31	97.95 53	94.58 37	94.38 163	26.89 366	94.56 46	99.39 40	93.57 50	99.05 96	98.93 79

APD-MVS_3200maxsize		96.82 10	96.65 22	97.32 22	97.95 80	93.82 29	96.31 41	98.25 19	95.51 31	96.99 62	97.05 96	95.63 19	99.39 40	93.31 63	98.88 110	98.75 99

zzz-MVS		96.47 31	96.14 40	97.47 11	98.95 14	94.05 18	93.69 133	97.62 77	94.46 41	96.29 86	96.94 99	93.56 57	99.37 45	94.29 35	99.42 56	98.99 70

MTAPA		96.65 21	96.38 31	97.47 11	98.95 14	94.05 18	95.88 55	97.62 77	94.46 41	96.29 86	96.94 99	93.56 57	99.37 45	94.29 35	99.42 56	98.99 70

SteuartSystems-ACMMP		96.40 37	96.30 33	96.71 39	98.63 28	91.96 51	95.70 59	98.01 44	93.34 62	96.64 73	96.57 123	94.99 41	99.36 47	93.48 55	99.34 66	98.82 92
Skip Steuart: Steuart Systems R&D Blog.
IS-MVSNet		94.49 107	94.35 108	94.92 91	98.25 60	86.46 130	97.13 14	94.31 236	96.24 23	96.28 89	96.36 142	82.88 232	99.35 48	88.19 174	99.52 45	98.96 76

UniMVSNet (Re)		95.32 69	95.15 87	95.80 63	97.79 85	88.91 87	92.91 156	98.07 36	93.46 59	96.31 84	95.97 164	90.14 134	99.34 49	92.11 97	99.64 26	99.16 47

HPM-MVS_fast		97.01 6	96.89 17	97.39 18	99.12 6	93.92 24	97.16 11	98.17 27	93.11 64	96.48 78	97.36 81	96.92 6	99.34 49	94.31 33	99.38 64	98.92 83

APD-MVS		95.00 83	94.69 97	95.93 56	97.38 108	90.88 66	94.59 101	97.81 64	89.22 163	95.46 128	96.17 158	93.42 62	99.34 49	89.30 152	98.87 113	97.56 177
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
v748		96.51 28	97.05 15	94.89 92	98.35 55	85.82 146	96.58 27	97.47 95	96.25 22	98.46 19	98.35 33	93.27 67	99.33 52	95.13 19	99.59 34	99.52 19

NR-MVSNet		95.28 73	95.28 82	95.26 81	97.75 87	87.21 119	95.08 80	97.37 102	93.92 51	97.65 38	95.90 165	90.10 138	99.33 52	90.11 139	99.66 23	99.26 40

SMA-MVS		95.77 54	95.54 69	96.47 47	98.27 58	91.19 61	95.09 79	97.79 68	86.48 219	97.42 48	97.51 71	94.47 50	99.29 54	93.55 52	99.29 72	98.93 79

FIs		94.90 88	95.35 77	93.55 140	98.28 57	81.76 190	95.33 71	98.14 29	93.05 65	97.07 56	97.18 88	87.65 180	99.29 54	91.72 110	99.69 15	99.61 11

LPG-MVS_test		96.38 39	96.23 36	96.84 37	98.36 53	92.13 48	95.33 71	98.25 19	91.78 107	97.07 56	97.22 86	96.38 11	99.28 56	92.07 100	99.59 34	99.11 52

LGP-MVS_train				96.84 37	98.36 53	92.13 48		98.25 19	91.78 107	97.07 56	97.22 86	96.38 11	99.28 56	92.07 100	99.59 34	99.11 52

HFP-MVS		96.39 38	96.17 39	97.04 28	98.51 43	93.37 35	96.30 43	97.98 47	92.35 84	95.63 121	96.47 128	95.37 23	99.27 58	93.78 45	99.14 88	98.48 115

#test#		95.89 51	95.51 70	97.04 28	98.51 43	93.37 35	95.14 77	97.98 47	89.34 159	95.63 121	96.47 128	95.37 23	99.27 58	91.99 102	99.14 88	98.48 115

thisisatest0530		88.69 242	87.52 254	92.20 198	96.33 175	79.36 240	92.81 159	84.01 344	86.44 220	93.67 182	92.68 269	53.62 359	99.25 60	89.65 148	98.45 151	98.00 141

ACMMP_Plus		96.21 44	96.12 42	96.49 46	98.90 16	91.42 58	94.57 104	98.03 41	90.42 142	96.37 81	97.35 82	95.68 18	99.25 60	94.44 31	99.34 66	98.80 94

HPM-MVS++		95.02 82	94.39 105	96.91 35	97.88 82	93.58 33	94.09 119	96.99 135	91.05 124	92.40 216	95.22 193	91.03 118	99.25 60	92.11 97	98.69 134	97.90 151

MVS_0304		92.99 155	92.54 164	94.35 118	94.67 263	86.06 141	91.16 226	97.92 57	90.01 148	88.33 300	94.41 221	87.02 195	99.22 63	90.36 130	99.00 102	97.76 163

CANet		92.38 176	91.99 175	93.52 144	93.82 285	83.46 170	91.14 227	97.00 133	89.81 153	86.47 319	94.04 236	87.90 178	99.21 64	89.50 150	98.27 170	97.90 151

LS3D		96.11 47	95.83 60	96.95 34	94.75 256	94.20 14	97.34 10	97.98 47	97.31 9	95.32 131	96.77 110	93.08 72	99.20 65	91.79 108	98.16 182	97.44 181

v13		95.39 66	96.12 42	93.18 151	97.22 113	80.81 202	95.55 65	97.57 84	93.42 60	98.02 29	98.49 26	89.62 145	99.18 66	95.54 12	99.68 18	99.54 15

v12		95.29 72	96.02 50	93.10 153	97.14 119	80.63 203	95.39 69	97.55 88	93.19 63	97.98 30	98.44 30	89.40 148	99.16 67	95.38 16	99.67 21	99.52 19

3Dnovator+	92.74 2	95.86 53	95.77 63	96.13 49	96.81 135	90.79 68	96.30 43	97.82 63	96.13 24	94.74 155	97.23 85	91.33 106	99.16 67	93.25 65	98.30 168	98.46 117

Anonymous20231211		96.60 24	97.13 13	95.00 89	97.46 107	86.35 135	97.11 15	98.24 22	97.58 6	98.72 8	98.97 7	93.15 71	99.15 69	93.18 69	99.74 14	99.50 21

v11		95.10 80	95.88 56	92.76 174	96.98 125	79.64 234	95.12 78	97.60 82	92.64 75	98.03 27	98.44 30	89.06 154	99.15 69	95.42 15	99.67 21	99.50 21

v10		94.68 100	95.27 83	92.90 167	96.57 151	80.15 210	94.65 99	97.57 84	90.68 134	97.43 46	98.00 47	88.18 166	99.15 69	94.84 24	99.55 42	99.41 28

V9		95.17 78	95.89 55	93.02 157	97.04 122	80.42 205	95.22 75	97.53 89	92.92 70	97.90 31	98.35 33	89.15 153	99.14 72	95.21 18	99.65 25	99.50 21

HyFIR lowres test		87.19 276	85.51 291	92.24 197	97.12 121	80.51 204	85.03 329	96.06 191	66.11 352	91.66 233	92.98 262	70.12 296	99.14 72	75.29 310	95.23 286	97.07 198

V14		95.05 81	95.75 64	92.94 164	96.94 127	80.21 208	95.03 83	97.50 93	92.62 76	97.84 33	98.28 37	88.87 156	99.13 74	95.03 20	99.64 26	99.48 24

test_0402		95.73 55	96.22 37	94.26 120	98.19 64	85.77 147	93.24 148	97.24 120	96.88 14	97.69 37	97.77 59	94.12 53	99.13 74	91.54 117	99.29 72	97.88 153

ACMP	88.15 13	95.71 56	95.43 76	96.54 43	98.17 65	91.73 56	94.24 116	98.08 33	89.46 157	96.61 75	96.47 128	95.85 16	99.12 76	90.45 125	99.56 41	98.77 98
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v15		94.93 86	95.62 68	92.86 169	96.83 133	80.01 221	94.84 91	97.48 94	92.36 82	97.76 35	98.20 39	88.61 157	99.11 77	94.86 22	99.62 29	99.46 25

lessismore_v0				93.87 133	98.05 72	83.77 168		80.32 362		97.13 55	97.91 53	77.49 271	99.11 77	92.62 87	98.08 191	98.74 100

v8		94.65 101	95.29 81	92.74 175	96.65 142	79.77 229	94.59 101	97.17 124	91.86 102	97.47 44	97.93 50	88.16 168	99.08 79	94.32 32	99.47 48	99.38 32

PVSNet_Blended_VisFu		91.63 187	91.20 195	92.94 164	97.73 91	83.95 166	92.14 190	97.46 96	78.85 294	92.35 219	94.98 203	84.16 226	99.08 79	86.36 201	96.77 247	95.79 251

v1240		93.29 141	93.71 132	92.06 204	96.01 199	77.89 266	91.81 212	97.37 102	85.12 236	96.69 71	96.40 134	86.67 204	99.07 81	94.51 29	98.76 129	99.22 43

v17		94.80 94	95.46 72	92.83 170	96.76 138	80.02 219	94.85 89	97.40 100	92.23 89	97.45 45	98.04 43	88.46 161	99.06 82	94.56 27	99.40 61	99.41 28

v16		94.79 96	95.44 75	92.83 170	96.73 139	80.03 217	94.85 89	97.41 99	92.23 89	97.41 49	98.04 43	88.40 163	99.06 82	94.56 27	99.30 70	99.41 28

v1921920		93.26 144	93.61 136	92.19 199	96.04 198	78.31 261	91.88 202	97.24 120	85.17 234	96.19 96	96.19 156	86.76 203	99.05 84	94.18 39	98.84 115	99.22 43

MIMVSNet1		95.52 61	95.45 73	95.72 67	99.14 3	89.02 85	96.23 46	96.87 149	93.73 53	97.87 32	98.49 26	90.73 124	99.05 84	86.43 200	99.60 32	99.10 55

DeepC-MVS	91.39 4	95.43 64	95.33 79	95.71 68	97.67 97	90.17 69	93.86 129	98.02 43	87.35 206	96.22 92	97.99 48	94.48 49	99.05 84	92.73 82	99.68 18	97.93 147
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v144192		93.20 149	93.54 139	92.16 201	96.05 195	78.26 262	91.95 195	97.14 125	84.98 240	95.96 104	96.11 159	87.08 194	99.04 87	93.79 44	98.84 115	99.17 46

Regformer-2		94.86 91	94.55 102	95.77 64	92.83 299	89.98 71	91.87 203	96.40 174	94.38 43	96.19 96	95.04 200	92.47 87	99.04 87	93.49 54	98.31 165	98.28 126

v18		94.63 102	95.26 84	92.74 175	96.60 149	79.81 227	94.64 100	97.37 102	91.87 101	97.26 52	97.91 53	88.13 169	99.04 87	94.30 34	99.24 78	99.38 32

WR-MVS		93.49 133	93.72 131	92.80 173	97.57 101	80.03 217	90.14 259	95.68 203	93.70 54	96.62 74	95.39 190	87.21 191	99.04 87	87.50 182	99.64 26	99.33 36

v1192		93.49 133	93.78 126	92.62 182	96.16 189	79.62 235	91.83 211	97.22 122	86.07 224	96.10 100	96.38 140	87.22 190	99.02 91	94.14 40	98.88 110	99.22 43

LCM-MVSNet-Re		94.20 117	94.58 101	93.04 155	95.91 212	83.13 177	93.79 130	99.19 2	92.00 97	98.84 5	98.04 43	93.64 56	99.02 91	81.28 251	98.54 142	96.96 203

Regformer-4		94.90 88	94.67 99	95.59 71	92.78 301	89.02 85	92.39 179	95.91 195	94.50 39	96.41 79	95.56 181	92.10 90	99.01 93	94.23 37	98.14 184	98.74 100

ACMM	88.83 9	96.30 42	96.07 46	96.97 32	98.39 49	92.95 41	94.74 95	98.03 41	90.82 131	97.15 54	96.85 107	96.25 13	99.00 94	93.10 73	99.33 68	98.95 77
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CPTT-MVS		94.74 97	94.12 116	96.60 41	98.15 66	93.01 39	95.84 56	97.66 75	89.21 164	93.28 193	95.46 184	88.89 155	98.98 95	89.80 145	98.82 121	97.80 161

GBi-Net		93.21 147	92.96 151	93.97 127	95.40 235	84.29 159	95.99 48	96.56 164	88.63 178	95.10 142	98.53 23	81.31 249	98.98 95	86.74 191	98.38 156	98.65 105

test1		93.21 147	92.96 151	93.97 127	95.40 235	84.29 159	95.99 48	96.56 164	88.63 178	95.10 142	98.53 23	81.31 249	98.98 95	86.74 191	98.38 156	98.65 105

FMVSNet1		94.84 92	95.13 88	93.97 127	97.60 99	84.29 159	95.99 48	96.56 164	92.38 80	97.03 61	98.53 23	90.12 135	98.98 95	88.78 164	99.16 86	98.65 105

Effi-MVS+-dtu		93.90 123	92.60 163	97.77 4	94.74 257	96.67 4	94.00 121	95.41 214	89.94 149	91.93 231	92.13 284	90.12 135	98.97 99	87.68 180	97.48 222	97.67 170

v1144		93.50 132	93.81 124	92.57 184	96.28 179	79.61 236	91.86 207	96.96 137	86.95 215	95.91 111	96.32 143	87.65 180	98.96 100	93.51 53	98.88 110	99.13 50

NCCC		94.08 119	93.54 139	95.70 69	96.49 156	89.90 73	92.39 179	96.91 145	90.64 135	92.33 222	94.60 217	90.58 129	98.96 100	90.21 136	97.70 209	98.23 128

nrg030		96.32 40	96.55 27	95.62 70	97.83 84	88.55 97	95.77 58	98.29 18	92.68 72	98.03 27	97.91 53	95.13 34	98.95 102	93.85 43	99.49 47	99.36 35

HQP_MVS		94.26 115	93.93 119	95.23 83	97.71 92	88.12 106	94.56 105	97.81 64	91.74 111	93.31 190	95.59 176	86.93 198	98.95 102	89.26 156	98.51 146	98.60 111

plane_prior5								97.81 64					98.95 102	89.26 156	98.51 146	98.60 111

v7		93.66 126	93.97 118	92.73 177	96.55 152	80.15 210	92.54 166	96.99 135	87.36 205	95.99 102	96.48 126	88.18 166	98.94 105	93.35 62	98.31 165	99.09 56

semantic-postprocess				91.94 206	93.89 282	79.22 247		93.51 251	91.53 116	95.37 130	96.62 120	77.17 274	98.90 106	91.89 107	94.95 290	97.70 167

v2v482		93.29 141	93.63 135	92.29 195	96.35 173	78.82 255	91.77 214	96.28 182	88.45 185	95.70 120	96.26 147	86.02 214	98.90 106	93.02 76	98.81 124	99.14 49

v1141		93.42 137	93.76 128	92.40 194	96.37 165	79.24 243	91.84 208	96.38 177	88.33 189	95.86 113	96.23 151	87.41 186	98.89 108	92.61 88	98.82 121	99.08 59

divwei89l23v2f112		93.42 137	93.76 128	92.41 192	96.37 165	79.24 243	91.84 208	96.38 177	88.33 189	95.86 113	96.23 151	87.41 186	98.89 108	92.61 88	98.83 118	99.09 56

v1		93.43 135	93.77 127	92.41 192	96.37 165	79.24 243	91.84 208	96.38 177	88.33 189	95.87 112	96.22 154	87.45 184	98.89 108	92.61 88	98.83 118	99.09 56

EPNet		89.80 224	88.25 237	94.45 114	83.91 367	86.18 138	93.87 128	87.07 314	91.16 123	80.64 354	94.72 214	78.83 261	98.89 108	85.17 209	98.89 108	98.28 126
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvs-test1		93.07 152	91.80 179	96.89 36	94.74 257	95.83 7	92.17 189	95.41 214	89.94 149	89.85 275	90.59 311	90.12 135	98.88 112	87.68 180	95.66 274	95.97 244

TEST9					96.45 162	89.46 76	90.60 242	96.92 142	79.09 292	90.49 260	94.39 224	91.31 107	98.88 112

train_agg		92.71 165	91.83 177	95.35 77	96.45 162	89.46 76	90.60 242	96.92 142	79.37 287	90.49 260	94.39 224	91.20 113	98.88 112	88.66 167	98.43 152	97.72 165

agg_prior3		92.56 172	91.62 182	95.35 77	96.39 164	89.45 78	90.61 241	96.82 150	78.82 295	90.03 268	94.14 233	90.72 125	98.88 112	88.66 167	98.43 152	97.72 165

CDPH-MVS		92.67 166	91.83 177	95.18 85	96.94 127	88.46 102	90.70 239	97.07 129	77.38 302	92.34 221	95.08 198	92.67 82	98.88 112	85.74 205	98.57 139	98.20 131

QAPM		92.88 159	92.77 156	93.22 150	95.82 214	83.31 172	96.45 33	97.35 110	83.91 248	93.75 179	96.77 110	89.25 150	98.88 112	84.56 222	97.02 238	97.49 179

v1neww		93.58 130	93.92 121	92.56 185	96.64 146	79.77 229	92.50 172	96.41 172	88.55 182	95.93 108	96.24 149	88.08 171	98.87 118	92.45 94	98.50 148	99.05 63

v7new		93.58 130	93.92 121	92.56 185	96.64 146	79.77 229	92.50 172	96.41 172	88.55 182	95.93 108	96.24 149	88.08 171	98.87 118	92.45 94	98.50 148	99.05 63

v6		93.59 129	93.93 119	92.56 185	96.65 142	79.77 229	92.50 172	96.40 174	88.55 182	95.94 107	96.23 151	88.13 169	98.87 118	92.46 93	98.50 148	99.06 62

EI-MVSNet-UG-set		94.35 111	94.27 113	94.59 105	92.46 305	85.87 144	92.42 178	94.69 229	93.67 58	96.13 98	95.84 169	91.20 113	98.86 121	93.78 45	98.23 175	99.03 66

EI-MVSNet-Vis-set		94.36 110	94.28 111	94.61 100	92.55 304	85.98 142	92.44 176	94.69 229	93.70 54	96.12 99	95.81 170	91.24 110	98.86 121	93.76 48	98.22 177	98.98 75

V42		93.43 135	93.58 137	92.97 161	95.34 240	81.22 196	92.67 163	96.49 169	87.25 208	96.20 94	96.37 141	87.32 189	98.85 123	92.39 96	98.21 178	98.85 90

Fast-Effi-MVS+		91.28 198	90.86 201	92.53 189	95.45 234	82.53 183	89.25 288	96.52 168	85.00 239	89.91 273	88.55 327	92.94 75	98.84 124	84.72 221	95.44 281	96.22 236

casdiffmvs1		93.02 154	93.00 150	93.07 154	95.65 224	82.54 182	94.79 94	97.35 110	80.09 279	92.18 225	97.51 71	89.25 150	98.84 124	92.65 85	97.52 216	97.83 157

TDRefinement		97.68 3	97.60 3	97.93 2	99.02 10	95.95 6	98.61 3	98.81 4	97.41 8	97.28 50	98.46 28	94.62 45	98.84 124	94.64 26	99.53 43	98.99 70

xiu_mvs_v1_base_debu		91.47 192	91.52 184	91.33 224	95.69 221	81.56 192	89.92 267	96.05 192	83.22 252	91.26 239	90.74 305	91.55 101	98.82 127	89.29 153	95.91 269	93.62 309

xiu_mvs_v1_base		91.47 192	91.52 184	91.33 224	95.69 221	81.56 192	89.92 267	96.05 192	83.22 252	91.26 239	90.74 305	91.55 101	98.82 127	89.29 153	95.91 269	93.62 309

xiu_mvs_v1_base_debi		91.47 192	91.52 184	91.33 224	95.69 221	81.56 192	89.92 267	96.05 192	83.22 252	91.26 239	90.74 305	91.55 101	98.82 127	89.29 153	95.91 269	93.62 309

test_8					96.37 165	89.14 83	90.51 246	96.89 146	79.37 287	90.42 262	94.36 226	91.20 113	98.82 127

PS-MVSNAJ		88.86 238	88.99 226	88.48 294	94.88 249	74.71 299	86.69 319	95.60 205	80.88 273	87.83 308	87.37 339	90.77 120	98.82 127	82.52 239	94.37 302	91.93 335

xiu_mvs_v2_base		89.00 234	89.19 220	88.46 295	94.86 251	74.63 301	86.97 315	95.60 205	80.88 273	87.83 308	88.62 326	91.04 117	98.81 132	82.51 240	94.38 301	91.93 335

FMVSNet2		92.78 162	92.73 159	92.95 163	95.40 235	81.98 187	94.18 118	95.53 211	88.63 178	96.05 101	97.37 79	81.31 249	98.81 132	87.38 186	98.67 135	98.06 137

Anonymous20240529		95.50 62	95.83 60	94.50 109	97.33 111	85.93 143	95.19 76	96.77 156	96.64 17	97.61 39	98.05 42	93.23 69	98.79 134	88.60 169	99.04 99	98.78 96

Regformer-1		94.55 105	94.33 109	95.19 84	92.83 299	88.54 98	91.87 203	95.84 199	93.99 47	95.95 105	95.04 200	92.00 92	98.79 134	93.14 72	98.31 165	98.23 128

VDD-MVS		94.37 109	94.37 107	94.40 116	97.49 105	86.07 140	93.97 123	93.28 254	94.49 40	96.24 90	97.78 57	87.99 176	98.79 134	88.92 161	99.14 88	98.34 121

test12				94.43 115	95.95 209	86.75 126		96.24 185		89.76 278		89.79 143	98.79 134		97.95 199	97.75 164

agg_prior1		92.60 168	91.76 180	95.10 87	96.20 185	88.89 88	90.37 250	96.88 147	79.67 284	90.21 263	94.41 221	91.30 108	98.78 138	88.46 171	98.37 161	97.64 172

agg_prior					96.20 185	88.89 88		96.88 147		90.21 263			98.78 138

CSCG		94.69 99	94.75 95	94.52 108	97.55 102	87.87 110	95.01 85	97.57 84	92.68 72	96.20 94	93.44 253	91.92 95	98.78 138	89.11 160	99.24 78	96.92 205

PHI-MVS		94.34 112	93.80 125	95.95 53	95.65 224	91.67 57	94.82 92	97.86 59	87.86 199	93.04 203	94.16 232	91.58 100	98.78 138	90.27 134	98.96 106	97.41 182

COLMAP_ROB	91.06 5	96.75 16	96.62 24	97.13 26	98.38 50	94.31 12	96.79 21	98.32 12	96.69 15	96.86 64	97.56 66	95.48 21	98.77 142	90.11 139	99.44 54	98.31 124
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
casdiffmvs		92.55 173	92.40 169	93.01 158	94.72 261	83.36 171	94.54 108	97.04 130	83.00 258	89.97 271	96.95 97	88.23 165	98.76 143	93.22 66	93.95 308	96.92 205

VDDNet		94.03 120	94.27 113	93.31 148	98.87 18	82.36 184	95.51 67	91.78 283	97.19 10	96.32 83	98.60 20	84.24 225	98.75 144	87.09 188	98.83 118	98.81 93

114514_t		90.51 206	89.80 215	92.63 181	98.00 76	82.24 185	93.40 139	97.29 116	65.84 353	89.40 283	94.80 210	86.99 196	98.75 144	83.88 227	98.61 137	96.89 208

FMVSNet3		90.78 203	90.32 210	92.16 201	93.03 296	79.92 223	92.54 166	94.95 220	86.17 223	95.10 142	96.01 162	69.97 297	98.75 144	86.74 191	98.38 156	97.82 160

IterMVS-LS		93.78 124	94.28 111	92.27 196	96.27 180	79.21 248	91.87 203	96.78 154	91.77 109	96.57 77	97.07 94	87.15 192	98.74 147	91.99 102	99.03 101	98.86 87
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DELS-MVS		92.05 183	92.16 171	91.72 212	94.44 270	80.13 213	87.62 305	97.25 119	87.34 207	92.22 224	93.18 260	89.54 147	98.73 148	89.67 147	98.20 180	96.30 233
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
thisisatest0515		84.72 300	82.99 307	89.90 257	92.96 297	75.33 297	84.36 336	83.42 346	77.37 303	88.27 303	86.65 341	53.94 357	98.72 149	82.56 238	97.40 226	95.67 256

alignmvs		93.26 144	92.85 154	94.50 109	95.70 220	87.45 114	93.45 138	95.76 200	91.58 114	95.25 136	92.42 278	81.96 242	98.72 149	91.61 113	97.87 203	97.33 189

MCST-MVS		92.91 158	92.51 165	94.10 123	97.52 103	85.72 148	91.36 223	97.13 127	80.33 277	92.91 207	94.24 228	91.23 111	98.72 149	89.99 143	97.93 200	97.86 155

XVG-ACMP-BASELINE		95.68 57	95.34 78	96.69 40	98.40 48	93.04 38	94.54 108	98.05 38	90.45 140	96.31 84	96.76 112	92.91 76	98.72 149	91.19 121	99.42 56	98.32 122

CNVR-MVS		94.58 104	94.29 110	95.46 76	96.94 127	89.35 82	91.81 212	96.80 152	89.66 155	93.90 177	95.44 186	92.80 80	98.72 149	92.74 81	98.52 144	98.32 122

DP-MVS		95.62 58	95.84 59	94.97 90	97.16 116	88.62 94	94.54 108	97.64 76	96.94 13	96.58 76	97.32 83	93.07 73	98.72 149	90.45 125	98.84 115	97.57 175

testing_2		94.03 120	94.38 106	93.00 159	96.79 137	81.41 195	92.87 158	96.96 137	85.88 228	97.06 59	97.92 51	91.18 116	98.71 155	91.72 110	99.04 99	98.87 86

原ACMM1				92.87 168	96.91 130	84.22 162		97.01 132	76.84 307	89.64 280	94.46 220	88.00 175	98.70 156	81.53 249	98.01 196	95.70 255

ANet_high		94.83 93	96.28 34	90.47 244	96.65 142	73.16 317	94.33 114	98.74 5	96.39 21	98.09 26	98.93 8	93.37 64	98.70 156	90.38 128	99.68 18	99.53 16

test_prior3		93.29 141	92.85 154	94.61 100	95.95 209	87.23 117	90.21 255	97.36 108	89.33 160	90.77 253	94.81 207	90.41 131	98.68 158	88.21 172	98.55 140	97.93 147

test_prior				94.61 100	95.95 209	87.23 117		97.36 108					98.68 158			97.93 147

Effi-MVS+		92.79 161	92.74 158	92.94 164	95.10 246	83.30 173	94.00 121	97.53 89	91.36 118	89.35 284	90.65 310	94.01 54	98.66 160	87.40 185	95.30 284	96.88 209

canonicalmvs		94.59 103	94.69 97	94.30 119	95.60 229	87.03 122	95.59 63	98.24 22	91.56 115	95.21 139	92.04 286	94.95 42	98.66 160	91.45 118	97.57 215	97.20 195

3Dnovator	92.54 3	94.80 94	94.90 92	94.47 112	95.47 233	87.06 121	96.63 24	97.28 118	91.82 106	94.34 166	97.41 76	90.60 128	98.65 162	92.47 92	98.11 188	97.70 167

ACMH+	88.43 11	96.48 30	96.82 18	95.47 75	98.54 39	89.06 84	95.65 62	98.61 6	96.10 25	98.16 24	97.52 69	96.90 7	98.62 163	90.30 133	99.60 32	98.72 103

HQP4-MVS										88.81 290			98.61 164			98.15 133

LTVRE_ROB	93.87 1	97.93 2	98.16 2	97.26 23	98.81 22	93.86 27	99.07 2	98.98 3	97.01 11	98.92 4	98.78 14	95.22 31	98.61 164	96.85 4	99.77 11	99.31 38
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
Fast-Effi-MVS+-dtu		92.77 163	92.16 171	94.58 107	94.66 264	88.25 104	92.05 192	96.65 161	89.62 156	90.08 266	91.23 296	92.56 83	98.60 166	86.30 202	96.27 264	96.90 207

HQP-MVS		92.09 182	91.49 187	93.88 132	96.36 170	84.89 155	91.37 220	97.31 113	87.16 209	88.81 290	93.40 254	84.76 222	98.60 166	86.55 197	97.73 206	98.14 134

无先验							89.94 266	95.75 201	70.81 337				98.59 168	81.17 254		94.81 277

1121		90.26 215	89.23 219	93.34 146	97.15 118	87.40 115	91.94 197	94.39 234	67.88 347	91.02 251	94.91 205	86.91 200	98.59 168	81.17 254	97.71 208	94.02 298

DeepC-MVS_fast	89.96 7	93.73 125	93.44 142	94.60 104	96.14 190	87.90 109	93.36 140	97.14 125	85.53 232	93.90 177	95.45 185	91.30 108	98.59 168	89.51 149	98.62 136	97.31 190
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CANet_DTU		89.85 222	89.17 221	91.87 208	92.20 311	80.02 219	90.79 236	95.87 197	86.02 225	82.53 343	91.77 289	80.01 257	98.57 171	85.66 206	97.70 209	97.01 201

view600		88.32 248	87.94 246	89.46 266	96.49 156	73.31 312	93.95 124	84.46 339	93.02 66	94.18 167	92.68 269	63.33 328	98.56 172	75.87 305	97.50 218	96.51 218

view800		88.32 248	87.94 246	89.46 266	96.49 156	73.31 312	93.95 124	84.46 339	93.02 66	94.18 167	92.68 269	63.33 328	98.56 172	75.87 305	97.50 218	96.51 218

conf0.05thres1000		88.32 248	87.94 246	89.46 266	96.49 156	73.31 312	93.95 124	84.46 339	93.02 66	94.18 167	92.68 269	63.33 328	98.56 172	75.87 305	97.50 218	96.51 218

tfpn		88.32 248	87.94 246	89.46 266	96.49 156	73.31 312	93.95 124	84.46 339	93.02 66	94.18 167	92.68 269	63.33 328	98.56 172	75.87 305	97.50 218	96.51 218

OPM-MVS		95.61 59	95.45 73	96.08 50	98.49 46	91.00 63	92.65 164	97.33 112	90.05 147	96.77 68	96.85 107	95.04 37	98.56 172	92.77 79	99.06 93	98.70 104

jason		89.17 231	88.32 235	91.70 213	95.73 219	80.07 214	88.10 302	93.22 256	71.98 329	90.09 265	92.79 264	78.53 265	98.56 172	87.43 184	97.06 236	96.46 227
jason: jason.
F-COLMAP		92.28 178	91.06 198	95.95 53	97.52 103	91.90 52	93.53 135	97.18 123	83.98 247	88.70 296	94.04 236	88.41 162	98.55 178	80.17 264	95.99 268	97.39 185

lupinMVS		88.34 247	87.31 256	91.45 221	94.74 257	80.06 215	87.23 311	92.27 274	71.10 333	88.83 288	91.15 297	77.02 276	98.53 179	86.67 194	96.75 248	95.76 252

PCF-MVS	84.52 17	89.12 232	87.71 251	93.34 146	96.06 194	85.84 145	86.58 322	97.31 113	68.46 345	93.61 183	93.89 241	87.51 183	98.52 180	67.85 342	98.11 188	95.66 257
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VPA-MVSNet		95.14 79	95.67 67	93.58 139	97.76 86	83.15 176	94.58 103	97.58 83	93.39 61	97.05 60	98.04 43	93.25 68	98.51 181	89.75 146	99.59 34	99.08 59

EI-MVSNet		92.99 155	93.26 148	92.19 199	92.12 313	79.21 248	92.32 183	94.67 231	91.77 109	95.24 137	95.85 167	87.14 193	98.49 182	91.99 102	98.26 171	98.86 87

MVSTER		89.32 228	88.75 231	91.03 234	90.10 335	76.62 279	90.85 234	94.67 231	82.27 265	95.24 137	95.79 171	61.09 338	98.49 182	90.49 124	98.26 171	97.97 146

UGNet		93.08 150	92.50 166	94.79 96	93.87 283	87.99 108	95.07 81	94.26 238	90.64 135	87.33 314	97.67 62	86.89 201	98.49 182	88.10 176	98.71 132	97.91 150
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
tfpn111		87.60 264	87.12 262	89.04 279	96.14 190	73.09 318	93.00 151	85.31 329	92.13 93	93.26 195	90.96 301	63.42 324	98.48 185	72.87 320	96.98 240	95.56 260

Regformer-3		94.28 113	94.23 115	94.46 113	92.78 301	86.28 136	92.39 179	94.70 228	93.69 57	95.97 103	95.56 181	91.34 105	98.48 185	93.45 57	98.14 184	98.62 109

Test4		91.41 196	91.25 194	91.89 207	95.35 239	80.32 206	90.97 231	96.92 142	81.96 267	95.11 141	93.81 243	81.34 248	98.48 185	88.71 166	97.08 235	96.87 210

test_normal		91.49 191	91.44 188	91.62 215	95.21 243	79.44 238	90.08 262	93.84 245	82.60 260	94.37 165	94.74 213	86.66 205	98.46 188	88.58 170	96.92 241	96.95 204

LFMVS		91.33 197	91.16 197	91.82 209	96.27 180	79.36 240	95.01 85	85.61 326	96.04 28	94.82 152	97.06 95	72.03 291	98.46 188	84.96 217	98.70 133	97.65 171

DI_MVS_plusplus_test		91.42 195	91.41 189	91.46 220	95.34 240	79.06 250	90.58 244	93.74 247	82.59 261	94.69 157	94.76 212	86.54 208	98.44 190	87.93 178	96.49 262	96.87 210

thres600view7		87.66 262	87.10 264	89.36 273	96.05 195	73.17 316	92.72 161	85.31 329	91.89 100	93.29 192	90.97 300	63.42 324	98.39 191	73.23 317	96.99 239	96.51 218

IB-MVS	77.21 19	83.11 307	81.05 319	89.29 274	91.15 320	75.85 287	85.66 326	86.00 321	79.70 283	82.02 348	86.61 342	48.26 364	98.39 191	77.84 288	92.22 330	93.63 308
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
v148		92.87 160	93.29 144	91.62 215	96.25 183	77.72 268	91.28 224	95.05 218	89.69 154	95.93 108	96.04 161	87.34 188	98.38 193	90.05 142	97.99 197	98.78 96

CDS-MVSNet		89.55 225	88.22 240	93.53 143	95.37 238	86.49 128	89.26 286	93.59 249	79.76 282	91.15 248	92.31 280	77.12 275	98.38 193	77.51 292	97.92 201	95.71 254
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
OpenMVS	89.45 8	92.27 179	92.13 173	92.68 179	94.53 269	84.10 164	95.70 59	97.03 131	82.44 264	91.14 249	96.42 132	88.47 160	98.38 193	85.95 204	97.47 223	95.55 264

MVS_Test		92.57 171	93.29 144	90.40 246	93.53 289	75.85 287	92.52 169	96.96 137	88.73 176	92.35 219	96.70 117	90.77 120	98.37 196	92.53 91	95.49 278	96.99 202

VPNet		93.08 150	93.76 128	91.03 234	98.60 32	75.83 289	91.51 218	95.62 204	91.84 103	95.74 117	97.10 93	89.31 149	98.32 197	85.07 216	99.06 93	98.93 79

AdaColmap		91.63 187	91.36 191	92.47 191	95.56 230	86.36 134	92.24 188	96.27 183	88.88 169	89.90 274	92.69 268	91.65 99	98.32 197	77.38 294	97.64 212	92.72 324

conf200view11		87.41 268	86.89 266	88.97 280	96.14 190	73.09 318	93.00 151	85.31 329	92.13 93	93.26 195	90.96 301	63.42 324	98.28 199	71.27 332	96.54 256	95.56 260

thres100view900		87.35 270	86.89 266	88.72 285	96.14 190	73.09 318	93.00 151	85.31 329	92.13 93	93.26 195	90.96 301	63.42 324	98.28 199	71.27 332	96.54 256	94.79 278

tfpn200view9		87.05 279	86.52 275	88.67 286	95.77 216	72.94 321	91.89 200	86.00 321	90.84 129	92.61 211	89.80 315	63.93 321	98.28 199	71.27 332	96.54 256	94.79 278

thres400		87.20 275	86.52 275	89.24 277	95.77 216	72.94 321	91.89 200	86.00 321	90.84 129	92.61 211	89.80 315	63.93 321	98.28 199	71.27 332	96.54 256	96.51 218

Vis-MVSNet (Re-imp)		90.42 208	90.16 211	91.20 232	97.66 98	77.32 273	94.33 114	87.66 309	91.20 121	92.99 204	95.13 196	75.40 283	98.28 199	77.86 287	99.19 83	97.99 142

PLC	85.34 15	90.40 209	88.92 227	94.85 93	96.53 154	90.02 70	91.58 216	96.48 170	80.16 278	86.14 321	92.18 282	85.73 216	98.25 204	76.87 297	94.61 299	96.30 233
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
新几何1				93.17 152	97.16 116	87.29 116		94.43 233	67.95 346	91.29 238	94.94 204	86.97 197	98.23 205	81.06 256	97.75 205	93.98 299

pmmvs6		96.80 13	97.36 8	95.15 86	99.12 6	87.82 112	96.68 23	97.86 59	96.10 25	98.14 25	99.28 2	97.94 3	98.21 206	91.38 120	99.69 15	99.42 27

wuykxyi23d		96.76 15	96.57 26	97.34 21	97.75 87	96.73 3	94.37 112	96.48 170	91.00 125	99.72 2	98.99 5	96.06 14	98.21 206	94.86 22	99.90 2	97.09 197

1112_ss		88.42 246	87.41 255	91.45 221	96.69 141	80.99 199	89.72 274	96.72 159	73.37 322	87.00 317	90.69 308	77.38 273	98.20 208	81.38 250	93.72 312	95.15 270

DP-MVS Recon		92.31 177	91.88 176	93.60 138	97.18 115	86.87 124	91.10 229	97.37 102	84.92 241	92.08 227	94.08 235	88.59 158	98.20 208	83.50 229	98.14 184	95.73 253

TAMVS		90.16 217	89.05 223	93.49 145	96.49 156	86.37 133	90.34 252	92.55 271	80.84 275	92.99 204	94.57 219	81.94 243	98.20 208	73.51 315	98.21 178	95.90 249

tfpnnormal		94.27 114	94.87 94	92.48 190	97.71 92	80.88 200	94.55 107	95.41 214	93.70 54	96.67 72	97.72 60	91.40 104	98.18 211	87.45 183	99.18 85	98.36 120

PVSNet_BlendedMVS		90.35 212	89.96 213	91.54 219	94.81 253	78.80 257	90.14 259	96.93 140	79.43 285	88.68 297	95.06 199	86.27 211	98.15 212	80.27 261	98.04 194	97.68 169

PVSNet_Blended		88.74 241	88.16 242	90.46 245	94.81 253	78.80 257	86.64 320	96.93 140	74.67 312	88.68 297	89.18 324	86.27 211	98.15 212	80.27 261	96.00 267	94.44 288

OMC-MVS		94.22 116	93.69 133	95.81 62	97.25 112	91.27 59	92.27 185	97.40 100	87.10 212	94.56 159	95.42 187	93.74 55	98.11 214	86.62 195	98.85 114	98.06 137

DeepPCF-MVS	90.46 6	94.20 117	93.56 138	96.14 48	95.96 208	92.96 40	89.48 279	97.46 96	85.14 235	96.23 91	95.42 187	93.19 70	98.08 215	90.37 129	98.76 129	97.38 187

0601test		90.11 218	89.73 217	91.26 228	94.09 278	79.82 225	90.44 247	92.65 268	90.90 127	93.19 200	93.30 256	73.90 285	98.03 216	82.23 242	96.87 243	95.93 246

Anonymous20240521		90.11 218	89.73 217	91.26 228	94.09 278	79.82 225	90.44 247	92.65 268	90.90 127	93.19 200	93.30 256	73.90 285	98.03 216	82.23 242	96.87 243	95.93 246

testdata2													98.03 216	80.24 263

thres200		85.85 293	85.18 293	87.88 302	94.44 270	72.52 323	89.08 290	86.21 318	88.57 181	91.44 236	88.40 328	64.22 319	98.00 219	68.35 341	95.88 272	93.12 317

ACMH	88.36 12	96.59 26	97.43 4	94.07 124	98.56 35	85.33 152	96.33 39	98.30 15	94.66 36	98.72 8	98.30 36	97.51 4	98.00 219	94.87 21	99.59 34	98.86 87
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAPA-MVS	88.58 10	92.49 174	91.75 181	94.73 97	96.50 155	89.69 75	92.91 156	97.68 74	78.02 299	92.79 208	94.10 234	90.85 119	97.96 221	84.76 220	98.16 182	96.54 216
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TransMVSNet (Re)		95.27 75	96.04 48	92.97 161	98.37 52	81.92 188	95.07 81	96.76 157	93.97 49	97.77 34	98.57 21	95.72 17	97.90 222	88.89 162	99.23 80	99.08 59

EG-PatchMatch MVS		94.54 106	94.67 99	94.14 122	97.87 83	86.50 127	92.00 194	96.74 158	88.16 194	96.93 63	97.61 64	93.04 74	97.90 222	91.60 114	98.12 187	98.03 139

conf0.01		86.95 281	86.04 281	89.70 261	95.99 201	75.66 290	93.28 141	82.70 349	88.81 170	91.26 239	88.01 332	58.77 343	97.89 224	78.93 276	96.60 250	95.56 260

conf0.002		86.95 281	86.04 281	89.70 261	95.99 201	75.66 290	93.28 141	82.70 349	88.81 170	91.26 239	88.01 332	58.77 343	97.89 224	78.93 276	96.60 250	95.56 260

thresconf0.02		86.69 286	86.04 281	88.64 288	95.99 201	75.66 290	93.28 141	82.70 349	88.81 170	91.26 239	88.01 332	58.77 343	97.89 224	78.93 276	96.60 250	92.36 328

tfpn_n400		86.69 286	86.04 281	88.64 288	95.99 201	75.66 290	93.28 141	82.70 349	88.81 170	91.26 239	88.01 332	58.77 343	97.89 224	78.93 276	96.60 250	92.36 328

tfpnconf		86.69 286	86.04 281	88.64 288	95.99 201	75.66 290	93.28 141	82.70 349	88.81 170	91.26 239	88.01 332	58.77 343	97.89 224	78.93 276	96.60 250	92.36 328

tfpnview11		86.69 286	86.04 281	88.64 288	95.99 201	75.66 290	93.28 141	82.70 349	88.81 170	91.26 239	88.01 332	58.77 343	97.89 224	78.93 276	96.60 250	92.36 328

BH-RMVSNet		90.47 207	90.44 208	90.56 242	95.21 243	78.65 259	89.15 289	93.94 244	88.21 192	92.74 209	94.22 229	86.38 209	97.88 230	78.67 283	95.39 282	95.14 271

Test_1112_low_res		87.50 267	86.58 272	90.25 250	96.80 136	77.75 267	87.53 309	96.25 184	69.73 341	86.47 319	93.61 247	75.67 282	97.88 230	79.95 266	93.20 317	95.11 272

MAR-MVS		90.32 214	88.87 230	94.66 99	94.82 252	91.85 53	94.22 117	94.75 226	80.91 272	87.52 313	88.07 331	86.63 206	97.87 232	76.67 298	96.21 266	94.25 291
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
tfpn1000		86.83 284	86.23 280	88.64 288	95.53 231	75.25 298	93.57 134	82.28 356	89.27 162	91.46 235	89.24 323	57.22 351	97.86 233	80.63 259	96.88 242	92.81 321

AllTest		94.88 90	94.51 103	96.00 51	98.02 74	92.17 46	95.26 74	98.43 8	90.48 138	95.04 146	96.74 114	92.54 84	97.86 233	85.11 214	98.98 103	97.98 143

TestCases				96.00 51	98.02 74	92.17 46		98.43 8	90.48 138	95.04 146	96.74 114	92.54 84	97.86 233	85.11 214	98.98 103	97.98 143

tfpn_ndepth		85.85 293	85.15 294	87.98 299	95.19 245	75.36 296	92.79 160	83.18 348	86.97 213	89.92 272	86.43 345	57.44 350	97.85 236	78.18 285	96.22 265	90.72 343

CLD-MVS		91.82 185	91.41 189	93.04 155	96.37 165	83.65 169	86.82 318	97.29 116	84.65 244	92.27 223	89.67 320	92.20 88	97.85 236	83.95 226	99.47 48	97.62 173
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TSAR-MVS + GP.		93.07 152	92.41 168	95.06 88	95.82 214	90.87 67	90.97 231	92.61 270	88.04 195	94.61 158	93.79 244	88.08 171	97.81 238	89.41 151	98.39 155	96.50 225

ambc				92.98 160	96.88 131	83.01 179	95.92 53	96.38 177		96.41 79	97.48 73	88.26 164	97.80 239	89.96 144	98.93 107	98.12 136

OpenMVS_ROB	85.12 16	89.52 227	89.05 223	90.92 238	94.58 268	81.21 197	91.10 229	93.41 253	77.03 306	93.41 187	93.99 240	83.23 229	97.80 239	79.93 268	94.80 294	93.74 306

BH-untuned		90.68 205	90.90 199	90.05 256	95.98 207	79.57 237	90.04 263	94.94 221	87.91 196	94.07 174	93.00 261	87.76 179	97.78 241	79.19 274	95.17 287	92.80 322

RPSCF		95.58 60	94.89 93	97.62 8	97.58 100	96.30 5	95.97 51	97.53 89	92.42 79	93.41 187	97.78 57	91.21 112	97.77 242	91.06 122	97.06 236	98.80 94

MVS_111021_HR		93.63 128	93.42 143	94.26 120	96.65 142	86.96 123	89.30 285	96.23 186	88.36 188	93.57 184	94.60 217	93.45 59	97.77 242	90.23 135	98.38 156	98.03 139

GA-MVS		87.70 260	86.82 268	90.31 247	93.27 292	77.22 275	84.72 333	92.79 264	85.11 237	89.82 276	90.07 312	66.80 306	97.76 244	84.56 222	94.27 305	95.96 245

Baseline_NR-MVSNet		94.47 108	95.09 90	92.60 183	98.50 45	80.82 201	92.08 191	96.68 160	93.82 52	96.29 86	98.56 22	90.10 138	97.75 245	90.10 141	99.66 23	99.24 42

MG-MVS		89.54 226	89.80 215	88.76 284	94.88 249	72.47 324	89.60 276	92.44 273	85.82 229	89.48 282	95.98 163	82.85 233	97.74 246	81.87 245	95.27 285	96.08 241

pm-mvs1		95.43 64	95.94 52	93.93 130	98.38 50	85.08 154	95.46 68	97.12 128	91.84 103	97.28 50	98.46 28	95.30 28	97.71 247	90.17 137	99.42 56	98.99 70

EPNet_dtu		85.63 295	84.37 297	89.40 272	86.30 360	74.33 306	91.64 215	88.26 302	84.84 243	72.96 365	89.85 313	71.27 293	97.69 248	76.60 299	97.62 213	96.18 238
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EU-MVSNet		87.39 269	86.71 271	89.44 270	93.40 290	76.11 284	94.93 88	90.00 295	57.17 362	95.71 119	97.37 79	64.77 318	97.68 249	92.67 84	94.37 302	94.52 285

CR-MVSNet		87.89 255	87.12 262	90.22 251	91.01 322	78.93 252	92.52 169	92.81 262	73.08 324	89.10 285	96.93 101	67.11 303	97.64 250	88.80 163	92.70 325	94.08 293

RPMNet		89.30 229	89.00 225	90.22 251	91.01 322	78.93 252	92.52 169	87.85 308	91.91 99	89.10 285	96.89 105	68.84 298	97.64 250	90.17 137	92.70 325	94.08 293

patchmatchnet-post											91.71 290	66.22 312	97.59 252

Patchmtry		90.11 218	89.92 214	90.66 240	90.35 333	77.00 277	92.96 154	92.81 262	90.25 145	94.74 155	96.93 101	67.11 303	97.52 253	85.17 209	98.98 103	97.46 180

Anonymous202405211		92.58 169	92.50 166	92.83 170	96.55 152	83.22 174	92.43 177	91.64 284	94.10 46	95.59 123	96.64 119	81.88 244	97.50 254	85.12 213	98.52 144	97.77 162

ab-mvs		92.40 175	92.62 162	91.74 211	97.02 123	81.65 191	95.84 56	95.50 212	86.95 215	92.95 206	97.56 66	90.70 126	97.50 254	79.63 270	97.43 224	96.06 242

FMVSNet5		87.82 259	86.56 273	91.62 215	92.31 307	79.81 227	93.49 136	94.81 225	83.26 251	91.36 237	96.93 101	52.77 360	97.49 256	76.07 302	98.03 195	97.55 178

diffmvs1		92.93 157	93.48 141	91.27 227	92.73 303	79.03 251	92.35 182	96.79 153	90.94 126	91.04 250	96.92 104	89.99 142	97.48 257	93.20 68	97.32 230	97.31 190

ppachtmachnet_test		88.61 243	88.64 232	88.50 293	91.76 316	70.99 330	84.59 334	92.98 259	79.30 291	92.38 217	93.53 250	79.57 259	97.45 258	86.50 199	97.17 233	97.07 198

IterMVS		90.18 216	90.16 211	90.21 253	93.15 294	75.98 286	87.56 308	92.97 260	86.43 221	94.09 172	96.40 134	78.32 266	97.43 259	87.87 179	94.69 297	97.23 193
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HY-MVS	82.50 18	86.81 285	85.93 288	89.47 265	93.63 287	77.93 264	94.02 120	91.58 285	75.68 309	83.64 336	93.64 245	77.40 272	97.42 260	71.70 328	92.07 332	93.05 318

TR-MVS		87.70 260	87.17 260	89.27 275	94.11 277	79.26 242	88.69 297	91.86 281	81.94 268	90.69 256	89.79 317	82.82 234	97.42 260	72.65 322	91.98 333	91.14 340

mvs_anonymous		90.37 211	91.30 193	87.58 304	92.17 312	68.00 338	89.84 272	94.73 227	83.82 250	93.22 199	97.40 77	87.54 182	97.40 262	87.94 177	95.05 289	97.34 188

MVP-Stereo		90.07 221	88.92 227	93.54 142	96.31 177	86.49 128	90.93 233	95.59 208	79.80 280	91.48 234	95.59 176	80.79 254	97.39 263	78.57 284	91.19 337	96.76 213
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
VNet		92.67 166	92.96 151	91.79 210	96.27 180	80.15 210	91.95 195	94.98 219	92.19 92	94.52 161	96.07 160	87.43 185	97.39 263	84.83 218	98.38 156	97.83 157

testdata				91.03 234	96.87 132	82.01 186		94.28 237	71.55 330	92.46 214	95.42 187	85.65 218	97.38 265	82.64 237	97.27 231	93.70 307

diffmvs		92.17 181	92.73 159	90.49 243	92.22 308	77.47 271	92.53 168	95.74 202	90.43 141	88.32 301	96.48 126	89.76 144	97.38 265	92.63 86	96.50 261	96.63 215

tpm		84.38 303	84.08 299	85.30 324	90.47 330	63.43 357	89.34 283	85.63 325	77.24 305	87.62 311	95.03 202	61.00 339	97.30 267	79.26 273	91.09 339	95.16 269

PAPM_NR		91.03 200	90.81 203	91.68 214	96.73 139	81.10 198	93.72 132	96.35 181	88.19 193	88.77 294	92.12 285	85.09 221	97.25 268	82.40 241	93.90 309	96.68 214

PAPM		81.91 318	80.11 328	87.31 307	93.87 283	72.32 325	84.02 339	93.22 256	69.47 342	76.13 362	89.84 314	72.15 290	97.23 269	53.27 361	89.02 342	92.37 327

gm-plane-assit					87.08 358	59.33 360			71.22 332		83.58 354		97.20 270	73.95 313

PAPR		87.65 263	86.77 270	90.27 249	92.85 298	77.38 272	88.56 299	96.23 186	76.82 308	84.98 327	89.75 319	86.08 213	97.16 271	72.33 323	93.35 315	96.26 235

CHOSEN 1792x2688		87.19 276	85.92 289	91.00 237	97.13 120	79.41 239	84.51 335	95.60 205	64.14 356	90.07 267	94.81 207	78.26 267	97.14 272	73.34 316	95.38 283	96.46 227

ITE_SJBPF				95.95 53	97.34 110	93.36 37		96.55 167	91.93 98	94.82 152	95.39 190	91.99 93	97.08 273	85.53 207	97.96 198	97.41 182

API-MVS		91.52 190	91.61 183	91.26 228	94.16 275	86.26 137	94.66 98	94.82 223	91.17 122	92.13 226	91.08 299	90.03 141	97.06 274	79.09 275	97.35 229	90.45 345

XVG-OURS-SEG-HR		95.38 67	95.00 91	96.51 44	98.10 70	94.07 15	92.46 175	98.13 32	90.69 133	93.75 179	96.25 148	98.03 2	97.02 275	92.08 99	95.55 276	98.45 118

XVG-OURS		94.72 98	94.12 116	96.50 45	98.00 76	94.23 13	91.48 219	98.17 27	90.72 132	95.30 132	96.47 128	87.94 177	96.98 276	91.41 119	97.61 214	98.30 125

cascas		87.02 280	86.28 279	89.25 276	91.56 319	76.45 280	84.33 337	96.78 154	71.01 334	86.89 318	85.91 347	81.35 247	96.94 277	83.09 233	95.60 275	94.35 290

MDA-MVSNet-bldmvs		91.04 199	90.88 200	91.55 218	94.68 262	80.16 209	85.49 327	92.14 278	90.41 143	94.93 150	95.79 171	85.10 220	96.93 278	85.15 211	94.19 307	97.57 175

BH-w/o		87.21 274	87.02 265	87.79 303	94.77 255	77.27 274	87.90 303	93.21 258	81.74 269	89.99 270	88.39 329	83.47 227	96.93 278	71.29 331	92.43 327	89.15 347

tpmp4_e23		81.87 319	80.41 324	86.27 316	89.29 343	67.84 339	91.58 216	87.61 310	67.42 348	78.60 358	92.71 267	56.42 354	96.87 280	71.44 330	88.63 344	94.10 292

CostFormer		83.09 308	82.21 310	85.73 318	89.27 344	67.01 341	90.35 251	86.47 317	70.42 338	83.52 338	93.23 259	61.18 337	96.85 281	77.21 295	88.26 346	93.34 316

pmmvs-eth3d		91.54 189	90.73 206	93.99 125	95.76 218	87.86 111	90.83 235	93.98 243	78.23 298	94.02 175	96.22 154	82.62 237	96.83 282	86.57 196	98.33 163	97.29 192

MVS		84.98 299	84.30 298	87.01 309	91.03 321	77.69 269	91.94 197	94.16 239	59.36 361	84.23 333	87.50 338	85.66 217	96.80 283	71.79 326	93.05 322	86.54 353

tpmvs		84.22 304	83.97 300	84.94 325	87.09 357	65.18 349	91.21 225	88.35 301	82.87 259	85.21 324	90.96 301	65.24 316	96.75 284	79.60 272	85.25 349	92.90 320

pmmvs5		87.87 256	87.14 261	90.07 255	93.26 293	76.97 278	88.89 294	92.18 275	73.71 321	88.36 299	93.89 241	76.86 279	96.73 285	80.32 260	96.81 245	96.51 218

CVMVSNet		85.16 297	84.72 295	86.48 313	92.12 313	70.19 332	92.32 183	88.17 305	56.15 363	90.64 257	95.85 167	67.97 301	96.69 286	88.78 164	90.52 340	92.56 325

tpm2		81.46 320	80.35 326	84.80 326	89.90 336	65.14 350	90.44 247	85.36 328	65.82 354	82.05 347	92.44 276	57.94 349	96.69 286	70.71 336	88.49 345	92.56 325

DWT-MVSNet_test		80.74 326	79.18 331	85.43 321	87.51 354	66.87 343	89.87 271	86.01 320	74.20 318	80.86 352	80.62 360	48.84 363	96.68 288	81.54 248	83.14 355	92.75 323

PatchmatchNet		85.22 296	84.64 296	86.98 310	89.51 341	69.83 335	90.52 245	87.34 312	78.87 293	87.22 315	92.74 266	66.91 305	96.53 289	81.77 246	86.88 348	94.58 284
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
旧先验2							90.00 265		68.65 344	92.71 210			96.52 290	85.15 211

new-patchmatchnet		88.97 235	90.79 204	83.50 334	94.28 274	55.83 364	85.34 328	93.56 250	86.18 222	95.47 126	95.73 174	83.10 230	96.51 291	85.40 208	98.06 192	98.16 132

ADS-MVSNet2		84.01 305	82.20 311	89.41 271	89.04 345	76.37 281	87.57 306	90.98 290	72.71 327	84.46 330	92.45 274	68.08 299	96.48 292	70.58 337	83.97 350	95.38 267

LP		86.29 291	85.35 292	89.10 278	87.80 350	76.21 282	89.92 267	90.99 289	84.86 242	87.66 310	92.32 279	70.40 295	96.48 292	81.94 244	82.24 357	94.63 283

TinyColmap		92.00 184	92.76 157	89.71 260	95.62 228	77.02 276	90.72 238	96.17 190	87.70 202	95.26 135	96.29 144	92.54 84	96.45 294	81.77 246	98.77 128	95.66 257

PatchFormer-LS_test		82.62 312	81.71 313	85.32 323	87.92 349	67.31 340	89.03 291	88.20 304	77.58 301	83.79 335	80.50 361	60.96 340	96.42 295	83.86 228	83.59 352	92.23 332

pmmvs4		88.95 236	87.70 252	92.70 178	94.30 273	85.60 149	87.22 312	92.16 277	74.62 313	89.75 279	94.19 230	77.97 269	96.41 296	82.71 236	96.36 263	96.09 240

USDC		89.02 233	89.08 222	88.84 283	95.07 247	74.50 304	88.97 292	96.39 176	73.21 323	93.27 194	96.28 145	82.16 239	96.39 297	77.55 291	98.80 125	95.62 259

MVS_111021_LR		93.66 126	93.28 146	94.80 95	96.25 183	90.95 64	90.21 255	95.43 213	87.91 196	93.74 181	94.40 223	92.88 78	96.38 298	90.39 127	98.28 169	97.07 198

PatchT		87.51 266	88.17 241	85.55 319	90.64 326	66.91 342	92.02 193	86.09 319	92.20 91	89.05 287	97.16 89	64.15 320	96.37 299	89.21 159	92.98 323	93.37 315

MSLP-MVS++		93.25 146	93.88 123	91.37 223	96.34 174	82.81 180	93.11 149	97.74 70	89.37 158	94.08 173	95.29 192	90.40 133	96.35 300	90.35 131	98.25 173	94.96 275

LF4IMVS		92.72 164	92.02 174	94.84 94	95.65 224	91.99 50	92.92 155	96.60 163	85.08 238	92.44 215	93.62 246	86.80 202	96.35 300	86.81 190	98.25 173	96.18 238

gg-mvs-nofinetune		82.10 316	81.02 320	85.34 322	87.46 355	71.04 328	94.74 95	67.56 368	96.44 20	79.43 357	98.99 5	45.24 365	96.15 302	67.18 344	92.17 331	88.85 349

JIA-IIPM		85.08 298	83.04 306	91.19 233	87.56 352	86.14 139	89.40 282	84.44 343	88.98 165	82.20 345	97.95 49	56.82 353	96.15 302	76.55 300	83.45 353	91.30 339

no-one		87.84 257	87.21 259	89.74 259	93.58 288	78.64 260	81.28 349	92.69 267	74.36 315	92.05 229	97.14 90	81.86 245	96.07 304	72.03 325	99.90 2	94.52 285

test_post1							90.21 255				5.85 370	65.36 314	96.00 305	79.61 271

PM-MVS		93.33 140	92.67 161	95.33 79	96.58 150	94.06 16	92.26 186	92.18 275	85.92 227	96.22 92	96.61 121	85.64 219	95.99 306	90.35 131	98.23 175	95.93 246

test_post											6.07 369	65.74 313	95.84 307

Patchmatch-test1		87.28 271	87.30 257	87.22 308	92.01 315	71.98 326	89.43 280	88.11 306	82.26 266	88.71 295	92.20 281	78.65 263	95.81 308	80.99 257	93.30 316	93.87 303

MSDG		90.82 201	90.67 207	91.26 228	94.16 275	83.08 178	86.63 321	96.19 189	90.60 137	91.94 230	91.89 287	89.16 152	95.75 309	80.96 258	94.51 300	94.95 276

our_test_3		87.55 265	87.59 253	87.44 306	91.76 316	70.48 331	83.83 340	90.55 294	79.79 281	92.06 228	92.17 283	78.63 264	95.63 310	84.77 219	94.73 295	96.22 236

MDTV_nov1_ep13			83.88 301		89.42 342	61.52 358	88.74 296	87.41 311	73.99 319	84.96 328	94.01 239	65.25 315	95.53 311	78.02 286	93.16 318

MIMVSNet		87.13 278	86.54 274	88.89 282	96.05 195	76.11 284	94.39 111	88.51 300	81.37 271	88.27 303	96.75 113	72.38 289	95.52 312	65.71 349	95.47 280	95.03 273

Gipuma		95.31 71	95.80 62	93.81 135	97.99 79	90.91 65	96.42 36	97.95 53	96.69 15	91.78 232	98.85 12	91.77 97	95.49 313	91.72 110	99.08 92	95.02 274
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	87.21 14	94.97 84	95.33 79	93.91 131	98.97 13	97.16 2	95.54 66	95.85 198	96.47 19	93.40 189	97.46 74	95.31 27	95.47 314	86.18 203	98.78 127	89.11 348
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dp		79.28 332	78.62 333	81.24 341	85.97 362	56.45 363	86.91 316	85.26 333	72.97 326	81.45 351	89.17 325	56.01 356	95.45 315	73.19 318	76.68 361	91.82 338

Anonymous20231206		88.77 240	88.29 236	90.20 254	96.31 177	78.81 256	89.56 278	93.49 252	74.26 317	92.38 217	95.58 179	82.21 238	95.43 316	72.07 324	98.75 131	96.34 231

CHOSEN 280x420		80.04 331	77.97 335	86.23 317	90.13 334	74.53 303	72.87 359	89.59 296	66.38 351	76.29 361	85.32 349	56.96 352	95.36 317	69.49 340	94.72 296	88.79 350

tpmrst		82.85 311	82.93 308	82.64 338	87.65 351	58.99 361	90.14 259	87.90 307	75.54 310	83.93 334	91.63 292	66.79 308	95.36 317	81.21 253	81.54 358	93.57 312

Patchmatch-RL test		88.81 239	88.52 233	89.69 263	95.33 242	79.94 222	86.22 323	92.71 266	78.46 296	95.80 115	94.18 231	66.25 311	95.33 319	89.22 158	98.53 143	93.78 304

tpm cat1		80.61 328	79.46 330	84.07 332	88.78 347	65.06 352	89.26 286	88.23 303	62.27 359	81.90 349	89.66 321	62.70 335	95.29 320	71.72 327	80.60 359	91.86 337

test20.03		90.80 202	90.85 202	90.63 241	95.63 227	79.24 243	89.81 273	92.87 261	89.90 151	94.39 162	96.40 134	85.77 215	95.27 321	73.86 314	99.05 96	97.39 185

testmv		88.46 245	88.11 243	89.48 264	96.00 200	76.14 283	86.20 324	93.75 246	84.48 245	93.57 184	95.52 183	80.91 253	95.09 322	63.97 351	98.61 137	97.22 194

1314		86.46 290	86.33 278	86.87 311	91.65 318	74.54 302	91.94 197	94.10 240	74.28 316	84.78 329	87.33 340	83.03 231	95.00 323	78.72 282	91.16 338	91.06 341

MVS-HIRNet		78.83 334	80.60 323	73.51 350	93.07 295	47.37 365	87.10 314	78.00 364	68.94 343	77.53 360	97.26 84	71.45 292	94.62 324	63.28 353	88.74 343	78.55 361

PVSNet	76.22 20	82.89 310	82.37 309	84.48 329	93.96 280	64.38 354	78.60 354	88.61 299	71.50 331	84.43 332	86.36 346	74.27 284	94.60 325	69.87 339	93.69 313	94.46 287

XXY-MVS		92.58 169	93.16 149	90.84 239	97.75 87	79.84 224	91.87 203	96.22 188	85.94 226	95.53 125	97.68 61	92.69 81	94.48 326	83.21 232	97.51 217	98.21 130

GG-mvs-BLEND				83.24 335	85.06 365	71.03 329	94.99 87	65.55 369		74.09 364	75.51 363	44.57 366	94.46 327	59.57 356	87.54 347	84.24 355

PatchMatch-RL		89.18 230	88.02 245	92.64 180	95.90 213	92.87 42	88.67 298	91.06 288	80.34 276	90.03 268	91.67 291	83.34 228	94.42 328	76.35 301	94.84 293	90.64 344

CNLPA		91.72 186	91.20 195	93.26 149	96.17 188	91.02 62	91.14 227	95.55 210	90.16 146	90.87 252	93.56 249	86.31 210	94.40 329	79.92 269	97.12 234	94.37 289

SD-MVS		95.19 76	95.73 65	93.55 140	96.62 148	88.88 90	94.67 97	98.05 38	91.26 119	97.25 53	96.40 134	95.42 22	94.36 330	92.72 83	99.19 83	97.40 184

UnsupCasMVSNet_bld		88.50 244	88.03 244	89.90 257	95.52 232	78.88 254	87.39 310	94.02 242	79.32 290	93.06 202	94.02 238	80.72 255	94.27 331	75.16 311	93.08 321	96.54 216

WTY-MVS		86.93 283	86.50 277	88.24 297	94.96 248	74.64 300	87.19 313	92.07 280	78.29 297	88.32 301	91.59 294	78.06 268	94.27 331	74.88 312	93.15 319	95.80 250

MS-PatchMatch		88.05 254	87.75 250	88.95 281	93.28 291	77.93 264	87.88 304	92.49 272	75.42 311	92.57 213	93.59 248	80.44 256	94.24 333	81.28 251	92.75 324	94.69 282

CMPMVS	68.83 22	87.28 271	85.67 290	92.09 203	88.77 348	85.42 151	90.31 253	94.38 235	70.02 340	88.00 306	93.30 256	73.78 287	94.03 334	75.96 304	96.54 256	96.83 212
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
YYNet1		88.17 252	88.24 238	87.93 300	92.21 310	73.62 309	80.75 350	88.77 298	82.51 263	94.99 148	95.11 197	82.70 235	93.70 335	83.33 230	93.83 310	96.48 226

MDA-MVSNet_test_wron		88.16 253	88.23 239	87.93 300	92.22 308	73.71 308	80.71 351	88.84 297	82.52 262	94.88 151	95.14 195	82.70 235	93.61 336	83.28 231	93.80 311	96.46 227

test-LLR		83.58 306	83.17 305	84.79 327	89.68 338	66.86 344	83.08 341	84.52 337	83.07 256	82.85 341	84.78 351	62.86 333	93.49 337	82.85 234	94.86 291	94.03 296

test-mter		81.21 323	80.01 329	84.79 327	89.68 338	66.86 344	83.08 341	84.52 337	73.85 320	82.85 341	84.78 351	43.66 368	93.49 337	82.85 234	94.86 291	94.03 296

pmmvs3		80.83 325	78.96 332	86.45 314	87.23 356	77.48 270	84.87 330	82.31 355	63.83 357	85.03 326	89.50 322	49.66 362	93.10 339	73.12 319	95.10 288	88.78 351

testgi		90.38 210	91.34 192	87.50 305	97.49 105	71.54 327	89.43 280	95.16 217	88.38 187	94.54 160	94.68 216	92.88 78	93.09 340	71.60 329	97.85 204	97.88 153

UnsupCasMVSNet_eth		90.33 213	90.34 209	90.28 248	94.64 265	80.24 207	89.69 275	95.88 196	85.77 230	93.94 176	95.69 175	81.99 241	92.98 341	84.21 224	91.30 336	97.62 173

test1235678		84.54 301	83.85 302	86.59 312	93.81 286	73.41 311	82.38 344	91.79 282	79.43 285	89.50 281	91.61 293	70.59 294	92.94 342	58.14 357	97.40 226	93.44 313

EPMVS		81.17 324	80.37 325	83.58 333	85.58 363	65.08 351	90.31 253	71.34 367	77.31 304	85.80 323	91.30 295	59.38 341	92.70 343	79.99 265	82.34 356	92.96 319

ADS-MVSNet		82.25 314	81.55 315	84.34 330	89.04 345	65.30 348	87.57 306	85.13 335	72.71 327	84.46 330	92.45 274	68.08 299	92.33 344	70.58 337	83.97 350	95.38 267

sss		87.23 273	86.82 268	88.46 295	93.96 280	77.94 263	86.84 317	92.78 265	77.59 300	87.61 312	91.83 288	78.75 262	91.92 345	77.84 288	94.20 306	95.52 265

N_pmnet		88.90 237	87.25 258	93.83 134	94.40 272	93.81 31	84.73 331	87.09 313	79.36 289	93.26 195	92.43 277	79.29 260	91.68 346	77.50 293	97.22 232	96.00 243

PMMVS		83.00 309	81.11 318	88.66 287	83.81 368	86.44 131	82.24 346	85.65 324	61.75 360	82.07 346	85.64 348	79.75 258	91.59 347	75.99 303	93.09 320	87.94 352

Patchmatch-test		86.10 292	86.01 287	86.38 315	90.63 327	74.22 307	89.57 277	86.69 315	85.73 231	89.81 277	92.83 263	65.24 316	91.04 348	77.82 290	95.78 273	93.88 302

1111		80.36 329	81.32 317	77.48 346	94.61 266	44.56 367	81.59 347	90.66 292	86.78 217	90.60 258	93.52 251	30.37 371	90.67 349	66.36 346	97.42 225	97.20 195

.test1245		64.72 341	70.88 342	46.22 354	94.61 266	44.56 367	81.59 347	90.66 292	86.78 217	90.60 258	93.52 251	30.37 371	90.67 349	66.36 346	3.45 367	3.44 367

TESTMET0.1,1		79.09 333	78.04 334	82.25 339	87.52 353	64.03 356	83.08 341	80.62 361	70.28 339	80.16 356	83.22 356	44.13 367	90.56 351	79.95 266	93.36 314	92.15 333

DSMNet-mixed		82.21 315	81.56 314	84.16 331	89.57 340	70.00 334	90.65 240	77.66 365	54.99 364	83.30 339	97.57 65	77.89 270	90.50 352	66.86 345	95.54 277	91.97 334

test12356		76.35 335	77.41 336	73.19 351	90.70 325	38.86 370	74.56 356	91.14 287	74.55 314	80.54 355	88.18 330	52.36 361	90.49 353	52.38 362	92.26 329	90.21 346

EMVS		80.35 330	80.28 327	80.54 342	84.73 366	69.07 336	72.54 360	80.73 360	87.80 200	81.66 350	81.73 358	62.89 332	89.84 354	75.79 309	94.65 298	82.71 358

PVSNet_0	70.34 21	74.58 337	72.96 339	79.47 344	90.63 327	66.24 347	73.26 357	83.40 347	63.67 358	78.02 359	78.35 362	72.53 288	89.59 355	56.68 358	60.05 364	82.57 359

testus		82.09 317	81.78 312	83.03 336	92.35 306	64.37 355	79.44 352	93.27 255	73.08 324	87.06 316	85.21 350	76.80 280	89.27 356	53.30 360	95.48 279	95.46 266

E-PMN		80.72 327	80.86 322	80.29 343	85.11 364	68.77 337	72.96 358	81.97 357	87.76 201	83.25 340	83.01 357	62.22 336	89.17 357	77.15 296	94.31 304	82.93 357

test0.0.03 1		82.48 313	81.47 316	85.48 320	89.70 337	73.57 310	84.73 331	81.64 358	83.07 256	88.13 305	86.61 342	62.86 333	89.10 358	66.24 348	90.29 341	93.77 305

test2356		75.58 336	73.13 338	82.95 337	86.10 361	66.42 346	75.07 355	84.87 336	70.91 335	80.85 353	80.66 359	38.02 370	88.98 359	49.32 363	92.35 328	93.44 313

PNet_i23d		72.03 340	70.91 341	75.38 348	90.46 331	57.84 362	71.73 361	81.53 359	83.86 249	82.21 344	83.49 355	29.97 373	87.80 360	60.78 354	54.12 365	80.51 360

FPMVS		84.50 302	83.28 304	88.16 298	96.32 176	94.49 11	85.76 325	85.47 327	83.09 255	85.20 325	94.26 227	63.79 323	86.58 361	63.72 352	91.88 335	83.40 356

new_pmnet		81.22 322	81.01 321	81.86 340	90.92 324	70.15 333	84.03 338	80.25 363	70.83 336	85.97 322	89.78 318	67.93 302	84.65 362	67.44 343	91.90 334	90.78 342

PMMVS2		81.31 321	83.44 303	74.92 349	90.52 329	46.49 366	69.19 362	85.23 334	84.30 246	87.95 307	94.71 215	76.95 278	84.36 363	64.07 350	98.09 190	93.89 301

testpf		74.01 338	76.37 337	66.95 352	80.56 369	60.00 359	88.43 301	75.07 366	81.54 270	75.75 363	83.73 353	38.93 369	83.09 364	84.01 225	79.32 360	57.75 363

wuyk23d		87.83 258	90.79 204	78.96 345	90.46 331	88.63 93	92.72 161	90.67 291	91.65 113	98.68 11	97.64 63	96.06 14	77.53 365	59.84 355	99.41 60	70.73 362

MVE	59.87 23	73.86 339	72.65 340	77.47 347	87.00 359	74.35 305	61.37 364	60.93 370	67.27 349	69.69 366	86.49 344	81.24 252	72.33 366	56.45 359	83.45 353	85.74 354
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				53.83 353	70.38 370	64.56 353		48.52 372	33.01 365	65.50 367	74.21 364	56.19 355	46.64 367	38.45 365	70.07 362	50.30 364

tmp_tt		37.97 344	44.33 344	18.88 356	11.80 371	21.54 371	63.51 363	45.66 373	4.23 366	51.34 368	50.48 365	59.08 342	22.11 368	44.50 364	68.35 363	13.00 365

test123		9.49 346	12.01 347	1.91 357	2.87 372	1.30 372	82.38 344	1.34 375	1.36 367	2.84 369	6.56 368	2.45 374	0.97 369	2.73 366	5.56 366	3.47 366

testmvs		9.02 347	11.42 348	1.81 358	2.77 373	1.13 373	79.44 352	1.90 374	1.18 368	2.65 370	6.80 367	1.95 375	0.87 370	2.62 367	3.45 367	3.44 367

test_part1				0.00 359		0.00 374	0.00 365	98.14 29				0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

v1.0		40.11 343	53.48 343	0.00 359	98.21 62	0.00 374	0.00 365	98.14 29	91.83 105	96.72 69	96.39 138	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

cdsmvs_eth3d_5k		23.35 345	31.13 346	0.00 359	0.00 374	0.00 374	0.00 365	95.58 209	0.00 369	0.00 371	91.15 297	93.43 61	0.00 371	0.00 368	0.00 369	0.00 369

pcd_1.5k_mvsjas		7.56 348	10.09 349	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	90.77 120	0.00 371	0.00 368	0.00 369	0.00 369

pcd1.5k->3k		41.03 342	43.65 345	33.18 355	98.74 25	0.00 374	0.00 365	97.57 84	0.00 369	0.00 371	0.00 371	97.01 5	0.00 371	0.00 368	99.52 45	99.53 16

sosnet-low-res		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sosnet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uncertanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

Regformer		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

ab-mvs-re		7.56 348	10.08 350	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	90.69 308	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

GSMVS																94.75 280

test_part2					98.21 62	89.41 79				96.72 69

sam_mvs1												66.64 309				94.75 280

sam_mvs												66.41 310

MTGPA								97.62 77

MTMP							94.82 92	54.62 371

test9_res														88.16 175	98.40 154	97.83 157

agg_prior2														87.06 189	98.36 162	97.98 143

test_prior4						89.91 72	90.74 237

test_prior2							90.21 255		89.33 160	90.77 253	94.81 207	90.41 131		88.21 172	98.55 140

新几何2							90.02 264

旧先验1					96.20 185	84.17 163		94.82 223			95.57 180	89.57 146			97.89 202	96.32 232

原ACMM2							89.34 283

test222					96.95 126	85.27 153	88.83 295	93.61 248	65.09 355	90.74 255	94.85 206	84.62 224			97.36 228	93.91 300

segment_acmp												92.14 89

testdata1							88.96 293		88.44 186

plane_prior7					97.71 92	88.68 92

plane_prior6					97.21 114	88.23 105						86.93 198

plane_prior4											95.59 176

plane_prior3						88.43 103			90.35 144	93.31 190

plane_prior2							94.56 105		91.74 111

plane_prior1					97.38 108

plane_prior						88.12 106	93.01 150		88.98 165						98.06 192

n2								0.00 376

nn								0.00 376

door-mid								92.13 279

test11								96.65 161

door								91.26 286

HQP5-MVS						84.89 155

HQP-NCC					96.36 170		91.37 220		87.16 209	88.81 290

ACMP_Plane					96.36 170		91.37 220		87.16 209	88.81 290

BP-MVS														86.55 197

HQP3-MVS								97.31 113							97.73 206

HQP2-MVS												84.76 222

NP-MVS					96.82 134	87.10 120					93.40 254

MDTV_nov1_ep13_2view						42.48 369	88.45 300		67.22 350	83.56 337		66.80 306		72.86 321		94.06 295

ACMMP++_ref															98.82 121

ACMMP++															99.25 77

Test By Simon												90.61 127

High-resolution multi-view benchmark