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.

SD-MVS		98.64 10	98.68 3	98.53 73	99.33 43	98.36 22	98.90 67	98.85 53	97.28 21	99.72 1	99.39 7	96.63 8	97.60 287	98.17 23	99.85 2	99.64 54

TSAR-MVS + MP.		98.78 3	98.62 4	99.24 25	99.69 17	98.28 28	99.14 38	98.66 107	96.84 43	99.56 2	99.31 21	96.34 11	99.70 91	98.32 20	99.73 35	99.73 28

HSP-MVS		98.70 5	98.52 8	99.24 25	99.75 3	98.23 29	99.26 17	98.58 120	97.52 7	99.41 3	98.78 85	96.00 24	99.79 69	97.79 38	99.59 53	99.69 36

APDe-MVS		99.02 1	98.84 1	99.55 1	99.57 24	98.96 3	99.39 5	98.93 36	97.38 17	99.41 3	99.54 1	96.66 6	99.84 42	98.86 2	99.85 2	99.87 1

SteuartSystems-ACMMP		98.90 2	98.75 2	99.36 12	99.22 72	98.43 17	99.10 45	98.87 49	97.38 17	99.35 5	99.40 6	97.78 1	99.87 35	97.77 39	99.85 2	99.78 7
Skip Steuart: Steuart Systems R&D Blog.
test_part2					99.63 21	99.18 1				99.27 6

abl_6		98.30 40	98.03 38	99.13 38	99.56 25	97.76 52	99.13 41	98.82 58	96.14 61	99.26 7	99.37 12	93.33 77	99.93 9	96.96 67	99.67 40	99.69 36

DeepPCF-MVS	96.37 2	97.93 48	98.48 13	96.30 223	99.00 87	89.54 285	97.43 255	98.87 49	98.16 2	99.26 7	99.38 11	96.12 18	99.64 100	98.30 21	99.77 18	99.72 31

APD-MVS		98.35 35	98.00 40	99.42 9	99.51 27	98.72 8	98.80 95	98.82 58	94.52 130	99.23 9	99.25 28	95.54 38	99.80 57	96.52 89	99.77 18	99.74 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Regformer-2		98.69 7	98.52 8	99.19 28	99.35 38	98.01 42	98.37 165	98.81 61	97.48 11	99.21 10	99.21 32	96.13 17	99.80 57	98.40 18	99.73 35	99.75 21

Regformer-1		98.66 8	98.51 10	99.12 40	99.35 38	97.81 51	98.37 165	98.76 75	97.49 10	99.20 11	99.21 32	96.08 20	99.79 69	98.42 16	99.73 35	99.75 21

APD-MVS_3200maxsize		98.53 25	98.33 24	99.15 37	99.50 28	97.92 46	99.15 37	98.81 61	96.24 58	99.20 11	99.37 12	95.30 44	99.80 57	97.73 41	99.67 40	99.72 31

ACMMP_Plus		98.61 13	98.30 25	99.55 1	99.62 22	98.95 4	98.82 86	98.81 61	95.80 72	99.16 13	99.47 4	95.37 41	99.92 13	97.89 32	99.75 30	99.79 4

Regformer-4		98.64 10	98.53 7	98.99 47	99.43 36	97.37 64	98.40 163	98.79 69	97.46 12	99.09 14	99.31 21	95.86 32	99.80 57	98.64 4	99.76 24	99.79 4

Regformer-3		98.59 16	98.50 11	98.86 57	99.43 36	97.05 75	98.40 163	98.68 97	97.43 13	99.06 15	99.31 21	95.80 33	99.77 79	98.62 6	99.76 24	99.78 7

PGM-MVS		98.49 27	98.23 32	99.27 23	99.72 11	98.08 39	98.99 57	99.49 5	95.43 86	99.03 16	99.32 20	95.56 36	99.94 3	96.80 79	99.77 18	99.78 7

VNet		97.79 54	97.40 61	98.96 51	98.88 99	97.55 58	98.63 132	98.93 36	96.74 46	99.02 17	98.84 80	90.33 128	99.83 43	98.53 10	96.66 157	99.50 72

xiu_mvs_v1_base_debu		97.60 61	97.56 51	97.72 119	98.35 128	95.98 112	97.86 227	98.51 132	97.13 34	99.01 18	98.40 117	91.56 107	99.80 57	98.53 10	98.68 103	97.37 193

xiu_mvs_v1_base		97.60 61	97.56 51	97.72 119	98.35 128	95.98 112	97.86 227	98.51 132	97.13 34	99.01 18	98.40 117	91.56 107	99.80 57	98.53 10	98.68 103	97.37 193

xiu_mvs_v1_base_debi		97.60 61	97.56 51	97.72 119	98.35 128	95.98 112	97.86 227	98.51 132	97.13 34	99.01 18	98.40 117	91.56 107	99.80 57	98.53 10	98.68 103	97.37 193

TSAR-MVS + GP.		98.38 32	98.24 31	98.81 58	99.22 72	97.25 70	98.11 199	98.29 167	97.19 30	98.99 21	99.02 58	96.22 12	99.67 96	98.52 14	98.56 111	99.51 70

HFP-MVS		98.63 12	98.40 14	99.32 16	99.72 11	98.29 26	99.23 22	98.96 31	96.10 65	98.94 22	99.17 39	96.06 21	99.92 13	97.62 45	99.78 14	99.75 21

region2R		98.61 13	98.38 16	99.29 18	99.74 7	98.16 35	99.23 22	98.93 36	96.15 60	98.94 22	99.17 39	95.91 29	99.94 3	97.55 50	99.79 10	99.78 7

#test#		98.54 24	98.27 27	99.32 16	99.72 11	98.29 26	98.98 60	98.96 31	95.65 78	98.94 22	99.17 39	96.06 21	99.92 13	97.21 60	99.78 14	99.75 21

HPM-MVS_fast		98.38 32	98.13 35	99.12 40	99.75 3	97.86 47	99.44 4	98.82 58	94.46 134	98.94 22	99.20 35	95.16 49	99.74 85	97.58 47	99.85 2	99.77 14

ACMMPR		98.59 16	98.36 18	99.29 18	99.74 7	98.15 36	99.23 22	98.95 33	96.10 65	98.93 26	99.19 38	95.70 34	99.94 3	97.62 45	99.79 10	99.78 7

DeepC-MVS_fast	96.70 1	98.55 22	98.34 21	99.18 32	99.25 65	98.04 40	98.50 153	98.78 71	97.72 4	98.92 27	99.28 25	95.27 45	99.82 48	97.55 50	99.77 18	99.69 36
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EI-MVSNet-Vis-set		98.47 28	98.39 15	98.69 62	99.46 33	96.49 97	98.30 176	98.69 94	97.21 28	98.84 28	99.36 16	95.41 40	99.78 74	98.62 6	99.65 44	99.80 3

MSLP-MVS++		98.56 21	98.57 5	98.55 71	99.26 64	96.80 84	98.71 117	99.05 23	97.28 21	98.84 28	99.28 25	96.47 10	99.40 131	98.52 14	99.70 38	99.47 78

PHI-MVS		98.34 36	98.06 37	99.18 32	99.15 79	98.12 38	99.04 53	99.09 19	93.32 181	98.83 30	99.10 48	96.54 9	99.83 43	97.70 43	99.76 24	99.59 62

MVSFormer		97.57 64	97.49 56	97.84 112	98.07 148	95.76 132	99.47 2	98.40 152	94.98 113	98.79 31	98.83 81	92.34 87	98.41 244	96.91 69	99.59 53	99.34 89

lupinMVS		97.44 71	97.22 68	98.12 98	98.07 148	95.76 132	97.68 241	97.76 227	94.50 131	98.79 31	98.61 100	92.34 87	99.30 137	97.58 47	99.59 53	99.31 92

CDPH-MVS		97.94 47	97.49 56	99.28 20	99.47 32	98.44 15	97.91 219	98.67 104	92.57 205	98.77 33	98.85 79	95.93 28	99.72 86	95.56 118	99.69 39	99.68 42

CNVR-MVS		98.78 3	98.56 6	99.45 8	99.32 46	98.87 6	98.47 156	98.81 61	97.72 4	98.76 34	99.16 42	97.05 3	99.78 74	98.06 25	99.66 43	99.69 36

EI-MVSNet-UG-set		98.41 30	98.34 21	98.61 67	99.45 34	96.32 104	98.28 178	98.68 97	97.17 31	98.74 35	99.37 12	95.25 46	99.79 69	98.57 8	99.54 65	99.73 28

UA-Net		97.96 45	97.62 48	98.98 49	98.86 101	97.47 61	98.89 71	99.08 20	96.67 49	98.72 36	99.54 1	93.15 80	99.81 50	94.87 134	98.83 99	99.65 51

旧先验2							97.57 249		91.30 249	98.67 37			99.80 57	95.70 115

PS-MVSNAJ		97.73 55	97.77 44	97.62 128	98.68 115	95.58 137	97.34 264	98.51 132	97.29 20	98.66 38	97.88 161	94.51 61	99.90 25	97.87 33	99.17 87	97.39 191

xiu_mvs_v2_base		97.66 60	97.70 47	97.56 136	98.61 121	95.46 143	97.44 253	98.46 142	97.15 32	98.65 39	98.15 141	94.33 67	99.80 57	97.84 36	98.66 107	97.41 189

LFMVS		95.86 129	94.98 151	98.47 78	98.87 100	96.32 104	98.84 83	96.02 312	93.40 178	98.62 40	99.20 35	74.99 313	99.63 103	97.72 42	97.20 149	99.46 82

HPM-MVS		98.36 34	98.10 36	99.13 38	99.74 7	97.82 50	99.53 1	98.80 68	94.63 127	98.61 41	98.97 65	95.13 50	99.77 79	97.65 44	99.83 7	99.79 4

testdata				98.26 89	99.20 75	95.36 146		98.68 97	91.89 229	98.60 42	99.10 48	94.44 66	99.82 48	94.27 151	99.44 75	99.58 64

CP-MVS		98.57 20	98.36 18	99.19 28	99.66 19	97.86 47	99.34 11	98.87 49	95.96 68	98.60 42	99.13 44	96.05 23	99.94 3	97.77 39	99.86 1	99.77 14

jason		97.32 79	97.08 73	98.06 104	97.45 187	95.59 136	97.87 226	97.91 223	94.79 120	98.55 44	98.83 81	91.12 115	99.23 143	97.58 47	99.60 50	99.34 89
jason: jason.
MCST-MVS		98.65 9	98.37 17	99.48 5	99.60 23	98.87 6	98.41 162	98.68 97	97.04 38	98.52 45	98.80 84	96.78 5	99.83 43	97.93 28	99.61 49	99.74 26

XVS		98.70 5	98.49 12	99.34 13	99.70 15	98.35 23	99.29 14	98.88 47	97.40 14	98.46 46	99.20 35	95.90 30	99.89 27	97.85 34	99.74 33	99.78 7

X-MVStestdata		94.06 235	92.30 253	99.34 13	99.70 15	98.35 23	99.29 14	98.88 47	97.40 14	98.46 46	43.50 342	95.90 30	99.89 27	97.85 34	99.74 33	99.78 7

MG-MVS		97.81 53	97.60 49	98.44 80	99.12 81	95.97 116	97.75 236	98.78 71	96.89 42	98.46 46	99.22 31	93.90 74	99.68 95	94.81 137	99.52 67	99.67 47

NCCC		98.61 13	98.35 20	99.38 10	99.28 61	98.61 11	98.45 157	98.76 75	97.82 3	98.45 49	98.93 73	96.65 7	99.83 43	97.38 57	99.41 77	99.71 33

MVS_Test		97.28 80	97.00 76	98.13 97	98.33 132	95.97 116	98.74 111	98.07 213	94.27 137	98.44 50	98.07 146	92.48 86	99.26 140	96.43 92	98.19 126	99.16 110

MVS_111021_LR		98.34 36	98.23 32	98.67 64	99.27 62	96.90 81	97.95 214	99.58 3	97.14 33	98.44 50	99.01 62	95.03 52	99.62 105	97.91 29	99.75 30	99.50 72

VDDNet		95.36 160	94.53 173	97.86 111	98.10 147	95.13 155	98.85 80	97.75 228	90.46 260	98.36 52	99.39 7	73.27 320	99.64 100	97.98 27	96.58 160	98.81 137

mPP-MVS		98.51 26	98.26 28	99.25 24	99.75 3	98.04 40	99.28 16	98.81 61	96.24 58	98.35 53	99.23 29	95.46 39	99.94 3	97.42 55	99.81 8	99.77 14

DELS-MVS		98.40 31	98.20 34	98.99 47	99.00 87	97.66 53	97.75 236	98.89 44	97.71 6	98.33 54	98.97 65	94.97 53	99.88 34	98.42 16	99.76 24	99.42 86
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
MVS_111021_HR		98.47 28	98.34 21	98.88 56	99.22 72	97.32 65	97.91 219	99.58 3	97.20 29	98.33 54	99.00 63	95.99 25	99.64 100	98.05 26	99.76 24	99.69 36

HPM-MVS++		98.58 18	98.25 29	99.55 1	99.50 28	99.08 2	98.72 116	98.66 107	97.51 8	98.15 56	98.83 81	95.70 34	99.92 13	97.53 52	99.67 40	99.66 49

新几何1				99.16 35	99.34 40	98.01 42		98.69 94	90.06 269	98.13 57	98.95 71	94.60 59	99.89 27	91.97 213	99.47 70	99.59 62

API-MVS		97.41 74	97.25 65	97.91 109	98.70 112	96.80 84	98.82 86	98.69 94	94.53 129	98.11 58	98.28 131	94.50 64	99.57 113	94.12 155	99.49 68	97.37 193

CPTT-MVS		97.72 56	97.32 63	98.92 53	99.64 20	97.10 74	99.12 43	98.81 61	92.34 219	98.09 59	99.08 54	93.01 81	99.92 13	96.06 99	99.77 18	99.75 21

test12				99.18 32	99.16 77	98.19 33		98.53 128		98.07 60		95.13 50	99.72 86		99.56 62	99.63 56

test222					99.23 71	97.17 73	97.40 256	98.66 107	88.68 293	98.05 61	98.96 69	94.14 70			99.53 66	99.61 57

DP-MVS Recon		97.86 51	97.46 58	99.06 45	99.53 26	98.35 23	98.33 169	98.89 44	92.62 202	98.05 61	98.94 72	95.34 43	99.65 98	96.04 100	99.42 76	99.19 105

Vis-MVSNet		97.42 73	97.11 71	98.34 86	98.66 116	96.23 107	99.22 28	99.00 26	96.63 51	98.04 63	99.21 32	88.05 186	99.35 136	96.01 102	99.21 85	99.45 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS_0304		97.70 57	97.25 65	99.07 43	98.90 96	97.83 49	98.20 184	98.74 79	97.51 8	98.03 64	99.06 56	86.12 224	99.93 9	99.02 1	99.64 46	99.44 85

MPTG		98.55 22	98.25 29	99.46 6	99.76 1	98.64 9	98.55 145	98.74 79	97.27 25	98.02 65	99.39 7	94.81 55	99.96 1	97.91 29	99.79 10	99.77 14

MTAPA		98.58 18	98.29 26	99.46 6	99.76 1	98.64 9	98.90 67	98.74 79	97.27 25	98.02 65	99.39 7	94.81 55	99.96 1	97.91 29	99.79 10	99.77 14

1121		97.37 77	96.77 87	99.16 35	99.34 40	97.99 45	98.19 188	98.68 97	90.14 267	98.01 67	98.97 65	94.80 57	99.87 35	93.36 172	99.46 73	99.61 57

sss		97.39 75	96.98 77	98.61 67	98.60 122	96.61 92	98.22 182	98.93 36	93.97 148	98.01 67	98.48 112	91.98 100	99.85 40	96.45 91	98.15 127	99.39 87

alignmvs		97.56 65	97.07 74	99.01 46	98.66 116	98.37 21	98.83 84	98.06 215	96.74 46	98.00 69	97.65 182	90.80 122	99.48 129	98.37 19	96.56 161	99.19 105

OMC-MVS		97.55 66	97.34 62	98.20 92	99.33 43	95.92 124	98.28 178	98.59 115	95.52 83	97.97 70	99.10 48	93.28 79	99.49 125	95.09 132	98.88 95	99.19 105

VDD-MVS		95.82 131	95.23 141	97.61 133	98.84 104	93.98 220	98.68 125	97.40 261	95.02 112	97.95 71	99.34 19	74.37 318	99.78 74	98.64 4	96.80 155	99.08 119

PVSNet_BlendedMVS		96.73 100	96.60 93	97.12 156	99.25 65	95.35 148	98.26 180	99.26 8	94.28 136	97.94 72	97.46 193	92.74 84	99.81 50	96.88 74	93.32 224	96.20 279

PVSNet_Blended		97.38 76	97.12 70	98.14 95	99.25 65	95.35 148	97.28 268	99.26 8	93.13 187	97.94 72	98.21 138	92.74 84	99.81 50	96.88 74	99.40 79	99.27 99

MP-MVS		98.33 38	98.01 39	99.28 20	99.75 3	98.18 34	99.22 28	98.79 69	96.13 62	97.92 74	99.23 29	94.54 60	99.94 3	96.74 81	99.78 14	99.73 28

MDTV_nov1_ep13_2view						84.26 316	96.89 286		90.97 257	97.90 75		89.89 133		93.91 159		99.18 109

test_prior3		98.22 42	97.90 43	99.19 28	99.31 48	98.22 31	97.80 232	98.84 54	96.12 63	97.89 76	98.69 92	95.96 26	99.70 91	96.89 71	99.60 50	99.65 51

test_prior2							97.80 232		96.12 63	97.89 76	98.69 92	95.96 26		96.89 71	99.60 50

原ACMM1				98.65 65	99.32 46	96.62 90		98.67 104	93.27 184	97.81 78	98.97 65	95.18 48	99.83 43	93.84 161	99.46 73	99.50 72

114514_t		96.93 93	96.27 104	98.92 53	99.50 28	97.63 55	98.85 80	98.90 42	84.80 314	97.77 79	99.11 46	92.84 82	99.66 97	94.85 135	99.77 18	99.47 78

PMMVS		96.60 103	96.33 102	97.41 142	97.90 159	93.93 221	97.35 263	98.41 150	92.84 198	97.76 80	97.45 195	91.10 117	99.20 145	96.26 95	97.91 133	99.11 115

PVSNet	91.96 18	96.35 113	96.15 108	96.96 165	99.17 76	92.05 253	96.08 303	98.68 97	93.69 166	97.75 81	97.80 171	88.86 154	99.69 94	94.26 152	99.01 90	99.15 111

TEST9					99.31 48	98.50 13	97.92 216	98.73 84	92.63 201	97.74 82	98.68 94	96.20 13	99.80 57

train_agg		97.97 44	97.52 54	99.33 15	99.31 48	98.50 13	97.92 216	98.73 84	92.98 192	97.74 82	98.68 94	96.20 13	99.80 57	96.59 85	99.57 56	99.68 42

CANet		98.05 43	97.76 45	98.90 55	98.73 109	97.27 67	98.35 167	98.78 71	97.37 19	97.72 84	98.96 69	91.53 111	99.92 13	98.79 3	99.65 44	99.51 70

test_8					99.29 56	98.44 15	97.89 224	98.72 86	92.98 192	97.70 85	98.66 97	96.20 13	99.80 57

MP-MVS-pluss		98.31 39	97.92 42	99.49 4	99.72 11	98.88 5	98.43 160	98.78 71	94.10 140	97.69 86	99.42 5	95.25 46	99.92 13	98.09 24	99.80 9	99.67 47
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs		97.67 59	97.23 67	98.98 49	98.70 112	98.38 18	99.34 11	98.39 154	96.76 45	97.67 87	97.40 197	92.26 90	99.49 125	98.28 22	96.28 178	99.08 119

PVSNet_Blended_VisFu		97.70 57	97.46 58	98.44 80	99.27 62	95.91 126	98.63 132	99.16 17	94.48 133	97.67 87	98.88 77	92.80 83	99.91 22	97.11 62	99.12 88	99.50 72

WTY-MVS		97.37 77	96.92 79	98.72 61	98.86 101	96.89 83	98.31 174	98.71 91	95.26 100	97.67 87	98.56 106	92.21 93	99.78 74	95.89 104	96.85 154	99.48 77

Effi-MVS+		97.12 87	96.69 89	98.39 84	98.19 141	96.72 88	97.37 260	98.43 149	93.71 163	97.65 90	98.02 149	92.20 94	99.25 141	96.87 77	97.79 139	99.19 105

HyFIR lowres test		96.90 95	96.49 98	98.14 95	99.33 43	95.56 139	97.38 258	99.65 2	92.34 219	97.61 91	98.20 139	89.29 139	99.10 159	96.97 65	97.60 145	99.77 14

ACMMP		98.23 41	97.95 41	99.09 42	99.74 7	97.62 56	99.03 54	99.41 6	95.98 67	97.60 92	99.36 16	94.45 65	99.93 9	97.14 61	98.85 98	99.70 35
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
agg_prior3		97.87 50	97.42 60	99.23 27	99.29 56	98.23 29	97.92 216	98.72 86	92.38 218	97.59 93	98.64 99	96.09 19	99.79 69	96.59 85	99.57 56	99.68 42

agg_prior1		97.95 46	97.51 55	99.28 20	99.30 53	98.38 18	97.81 231	98.72 86	93.16 186	97.57 94	98.66 97	96.14 16	99.81 50	96.63 84	99.56 62	99.66 49

agg_prior					99.30 53	98.38 18		98.72 86		97.57 94			99.81 50

tpmrst		95.63 140	95.69 125	95.44 254	97.54 179	88.54 301	96.97 278	97.56 235	93.50 175	97.52 96	96.93 242	89.49 134	99.16 147	95.25 129	96.42 166	98.64 148

MDTV_nov1_ep13			95.40 129		97.48 182	88.34 303	96.85 288	97.29 270	93.74 160	97.48 97	97.26 207	89.18 142	99.05 163	91.92 215	97.43 147

EPMVS		94.99 176	94.48 174	96.52 206	97.22 200	91.75 259	97.23 270	91.66 339	94.11 139	97.28 98	96.81 251	85.70 232	98.84 191	93.04 182	97.28 148	98.97 127

IS-MVSNet		97.22 82	96.88 80	98.25 90	98.85 103	96.36 102	99.19 34	97.97 220	95.39 88	97.23 99	98.99 64	91.11 116	98.93 180	94.60 141	98.59 109	99.47 78

EPP-MVSNet		97.46 67	97.28 64	97.99 106	98.64 118	95.38 145	99.33 13	98.31 162	93.61 172	97.19 100	99.07 55	94.05 71	99.23 143	96.89 71	98.43 118	99.37 88

CANet_DTU		96.96 92	96.55 95	98.21 91	98.17 145	96.07 111	97.98 211	98.21 178	97.24 27	97.13 101	98.93 73	86.88 213	99.91 22	95.00 133	99.37 81	98.66 146

CHOSEN 1792x2688		97.12 87	96.80 82	98.08 101	99.30 53	94.56 204	98.05 204	99.71 1	93.57 173	97.09 102	98.91 76	88.17 181	99.89 27	96.87 77	99.56 62	99.81 2

PatchT		93.06 254	91.97 256	96.35 219	96.69 231	92.67 246	94.48 324	97.08 278	86.62 302	97.08 103	92.23 324	87.94 188	97.90 278	78.89 318	96.69 156	98.49 154

PatchmatchNet		95.71 136	95.52 128	96.29 224	97.58 176	90.72 272	96.84 289	97.52 241	94.06 142	97.08 103	96.96 235	89.24 141	98.90 185	92.03 211	98.37 119	99.26 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MAR-MVS		96.91 94	96.40 100	98.45 79	98.69 114	96.90 81	98.66 130	98.68 97	92.40 217	97.07 105	97.96 154	91.54 110	99.75 83	93.68 165	98.92 93	98.69 143
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
PAPM_NR		97.46 67	97.11 71	98.50 75	99.50 28	96.41 100	98.63 132	98.60 114	95.18 104	97.06 106	98.06 147	94.26 69	99.57 113	93.80 163	98.87 97	99.52 67

TAMVS		97.02 90	96.79 84	97.70 122	98.06 150	95.31 150	98.52 148	98.31 162	93.95 149	97.05 107	98.61 100	93.49 76	98.52 220	95.33 124	97.81 138	99.29 97

CSCG		97.85 52	97.74 46	98.20 92	99.67 18	95.16 153	99.22 28	99.32 7	93.04 189	97.02 108	98.92 75	95.36 42	99.91 22	97.43 54	99.64 46	99.52 67

tfpn_ndepth		95.53 147	94.90 160	97.39 147	98.96 93	95.88 129	99.05 51	95.27 321	93.80 157	96.95 109	96.93 242	85.53 234	99.40 131	91.54 224	96.10 186	96.89 216

CDS-MVSNet		96.99 91	96.69 89	97.90 110	98.05 151	95.98 112	98.20 184	98.33 161	93.67 170	96.95 109	98.49 111	93.54 75	98.42 237	95.24 130	97.74 142	99.31 92
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
XVG-OURS-SEG-HR		96.51 108	96.34 101	97.02 161	98.77 107	93.76 226	97.79 234	98.50 137	95.45 85	96.94 111	99.09 52	87.87 192	99.55 121	96.76 80	95.83 190	97.74 180

CR-MVSNet		94.76 191	94.15 191	96.59 196	97.00 212	93.43 234	94.96 317	97.56 235	92.46 206	96.93 112	96.24 270	88.15 182	97.88 282	87.38 289	96.65 158	98.46 155

RPMNet		92.52 258	91.17 261	96.59 196	97.00 212	93.43 234	94.96 317	97.26 273	82.27 321	96.93 112	92.12 325	86.98 211	97.88 282	76.32 323	96.65 158	98.46 155

Patchmatch-test1		95.32 164	94.97 153	96.35 219	97.67 169	91.29 265	97.33 265	97.60 233	94.68 122	96.92 114	96.95 236	83.97 266	98.50 223	91.33 229	98.32 122	99.25 101

PatchMatch-RL		96.59 105	96.03 112	98.27 88	99.31 48	96.51 96	97.91 219	99.06 21	93.72 162	96.92 114	98.06 147	88.50 176	99.65 98	91.77 219	99.00 91	98.66 146

DeepC-MVS	95.98 3	97.88 49	97.58 50	98.77 59	99.25 65	96.93 79	98.83 84	98.75 78	96.96 41	96.89 116	99.50 3	90.46 125	99.87 35	97.84 36	99.76 24	99.52 67
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
diffmvs		96.32 115	95.74 118	98.07 103	98.26 135	96.14 109	98.53 147	98.23 176	90.10 268	96.88 117	97.73 174	90.16 131	99.15 148	93.90 160	97.85 137	98.91 133

tfpn1000		95.72 134	95.11 145	97.58 134	99.00 87	95.73 134	99.24 20	95.49 320	94.08 141	96.87 118	97.45 195	85.81 230	99.30 137	91.78 218	96.22 183	97.71 183

XVG-OURS		96.55 107	96.41 99	96.99 162	98.75 108	93.76 226	97.50 252	98.52 130	95.67 76	96.83 119	99.30 24	88.95 151	99.53 122	95.88 105	96.26 179	97.69 184

AdaColmap		97.15 86	96.70 88	98.48 77	99.16 77	96.69 89	98.01 208	98.89 44	94.44 135	96.83 119	98.68 94	90.69 123	99.76 81	94.36 147	99.29 84	98.98 126

CostFormer		94.95 180	94.73 165	95.60 247	97.28 196	89.06 292	97.53 250	96.89 296	89.66 282	96.82 121	96.72 254	86.05 226	98.95 179	95.53 119	96.13 185	98.79 138

UGNet		96.78 99	96.30 103	98.19 94	98.24 136	95.89 128	98.88 73	98.93 36	97.39 16	96.81 122	97.84 165	82.60 275	99.90 25	96.53 88	99.49 68	98.79 138
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
CNLPA		97.45 70	97.03 75	98.73 60	99.05 82	97.44 63	98.07 203	98.53 128	95.32 98	96.80 123	98.53 107	93.32 78	99.72 86	94.31 150	99.31 83	99.02 122

CHOSEN 280x420		97.18 84	97.18 69	97.20 150	98.81 105	93.27 237	95.78 311	99.15 18	95.25 101	96.79 124	98.11 144	92.29 89	99.07 162	98.56 9	99.85 2	99.25 101

HY-MVS	93.96 8	96.82 98	96.23 107	98.57 69	98.46 127	97.00 76	98.14 194	98.21 178	93.95 149	96.72 125	97.99 153	91.58 106	99.76 81	94.51 145	96.54 162	98.95 131

PAPR		96.84 97	96.24 106	98.65 65	98.72 111	96.92 80	97.36 262	98.57 121	93.33 180	96.67 126	97.57 189	94.30 68	99.56 115	91.05 234	98.59 109	99.47 78

thres600view7		95.49 148	94.77 163	97.67 125	98.98 90	95.02 158	98.85 80	96.90 293	95.38 89	96.63 127	96.90 244	84.29 257	99.59 107	88.65 277	96.33 172	98.40 158

conf200view11		95.40 156	94.70 166	97.50 138	98.98 90	94.92 166	98.87 74	96.90 293	95.38 89	96.61 128	96.88 247	84.29 257	99.56 115	88.11 283	96.29 174	98.02 173

thres100view900		95.38 157	94.70 166	97.41 142	98.98 90	94.92 166	98.87 74	96.90 293	95.38 89	96.61 128	96.88 247	84.29 257	99.56 115	88.11 283	96.29 174	97.76 178

Vis-MVSNet (Re-imp)		96.87 96	96.55 95	97.83 113	98.73 109	95.46 143	99.20 32	98.30 165	94.96 115	96.60 130	98.87 78	90.05 132	98.59 209	93.67 166	98.60 108	99.46 82

CVMVSNet		95.43 152	96.04 111	93.57 294	97.93 157	83.62 317	98.12 197	98.59 115	95.68 75	96.56 131	99.02 58	87.51 202	97.51 290	93.56 169	97.44 146	99.60 60

RPSCF		94.87 184	95.40 129	93.26 298	98.89 98	82.06 323	98.33 169	98.06 215	90.30 264	96.56 131	99.26 27	87.09 208	99.49 125	93.82 162	96.32 173	98.24 168

tfpn200view9		95.32 164	94.62 169	97.43 141	98.94 94	94.98 162	98.68 125	96.93 291	95.33 96	96.55 133	96.53 261	84.23 261	99.56 115	88.11 283	96.29 174	97.76 178

thres400		95.38 157	94.62 169	97.65 127	98.94 94	94.98 162	98.68 125	96.93 291	95.33 96	96.55 133	96.53 261	84.23 261	99.56 115	88.11 283	96.29 174	98.40 158

thres200		95.25 166	94.57 171	97.28 148	98.81 105	94.92 166	98.20 184	97.11 277	95.24 103	96.54 135	96.22 274	84.58 249	99.53 122	87.93 287	96.50 164	97.39 191

ab-mvs		96.42 111	95.71 123	98.55 71	98.63 119	96.75 87	97.88 225	98.74 79	93.84 154	96.54 135	98.18 140	85.34 239	99.75 83	95.93 103	96.35 171	99.15 111

view600		95.60 143	94.93 155	97.62 128	99.05 82	94.85 171	99.09 46	97.01 285	95.36 92	96.52 137	97.37 198	84.55 250	99.59 107	89.07 268	96.39 167	98.40 158

view800		95.60 143	94.93 155	97.62 128	99.05 82	94.85 171	99.09 46	97.01 285	95.36 92	96.52 137	97.37 198	84.55 250	99.59 107	89.07 268	96.39 167	98.40 158

conf0.05thres1000		95.60 143	94.93 155	97.62 128	99.05 82	94.85 171	99.09 46	97.01 285	95.36 92	96.52 137	97.37 198	84.55 250	99.59 107	89.07 268	96.39 167	98.40 158

tfpn		95.60 143	94.93 155	97.62 128	99.05 82	94.85 171	99.09 46	97.01 285	95.36 92	96.52 137	97.37 198	84.55 250	99.59 107	89.07 268	96.39 167	98.40 158

mvs-test1		96.60 103	96.68 91	96.37 217	97.89 160	91.81 256	98.56 143	98.10 208	96.57 52	96.52 137	97.94 156	90.81 120	99.45 130	95.72 111	98.01 130	97.86 177

ADS-MVSNet2		94.58 207	94.40 180	95.11 268	98.00 152	88.74 296	96.04 304	97.30 269	90.15 265	96.47 142	96.64 258	87.89 190	97.56 289	90.08 247	97.06 150	99.02 122

ADS-MVSNet		95.00 175	94.45 178	96.63 191	98.00 152	91.91 255	96.04 304	97.74 229	90.15 265	96.47 142	96.64 258	87.89 190	98.96 175	90.08 247	97.06 150	99.02 122

Effi-MVS+-dtu		96.29 116	96.56 94	95.51 248	97.89 160	90.22 279	98.80 95	98.10 208	96.57 52	96.45 144	96.66 256	90.81 120	98.91 182	95.72 111	97.99 131	97.40 190

PLC	95.07 4	97.20 83	96.78 85	98.44 80	99.29 56	96.31 106	98.14 194	98.76 75	92.41 216	96.39 145	98.31 130	94.92 54	99.78 74	94.06 156	98.77 102	99.23 103
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm		94.13 231	93.80 213	95.12 267	96.50 239	87.91 307	97.44 253	95.89 317	92.62 202	96.37 146	96.30 269	84.13 264	98.30 257	93.24 175	91.66 244	99.14 113

TAPA-MVS	93.98 7	95.35 161	94.56 172	97.74 118	99.13 80	94.83 182	98.33 169	98.64 112	86.62 302	96.29 147	98.61 100	94.00 73	99.29 139	80.00 314	99.41 77	99.09 116
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tpm2		94.19 225	93.76 218	95.46 252	97.23 199	89.04 293	97.31 267	96.85 299	87.08 301	96.21 148	96.79 252	83.75 271	98.74 199	92.43 203	96.23 181	98.59 150

F-COLMAP		97.09 89	96.80 82	97.97 107	99.45 34	94.95 165	98.55 145	98.62 113	93.02 190	96.17 149	98.58 105	94.01 72	99.81 50	93.95 158	98.90 94	99.14 113

PatchFormer-LS_test		95.47 149	95.27 140	96.08 232	97.59 175	90.66 273	98.10 201	97.34 265	93.98 147	96.08 150	96.15 276	87.65 200	99.12 152	95.27 128	95.24 194	98.44 157

JIA-IIPM		93.35 246	92.49 250	95.92 235	96.48 241	90.65 274	95.01 316	96.96 289	85.93 308	96.08 150	87.33 329	87.70 198	98.78 198	91.35 228	95.58 192	98.34 165

BH-RMVSNet		95.92 127	95.32 137	97.69 123	98.32 133	94.64 196	98.19 188	97.45 256	94.56 128	96.03 152	98.61 100	85.02 242	99.12 152	90.68 238	99.06 89	99.30 95

dp		94.15 230	93.90 208	94.90 272	97.31 195	86.82 313	96.97 278	97.19 276	91.22 254	96.02 153	96.61 260	85.51 235	99.02 170	90.00 251	94.30 197	98.85 134

EPNet		97.28 80	96.87 81	98.51 74	94.98 303	96.14 109	98.90 67	97.02 283	98.28 1	95.99 154	99.11 46	91.36 112	99.89 27	96.98 64	99.19 86	99.50 72
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		97.16 85	96.66 92	98.68 63	98.53 126	97.19 72	98.93 65	98.90 42	92.83 199	95.99 154	99.37 12	92.12 96	99.87 35	93.67 166	99.57 56	98.97 127

TR-MVS		94.94 182	94.20 188	97.17 153	97.75 166	94.14 217	97.59 247	97.02 283	92.28 223	95.75 156	97.64 184	83.88 268	98.96 175	89.77 253	96.15 184	98.40 158

VPA-MVSNet		95.75 133	95.11 145	97.69 123	97.24 198	97.27 67	98.94 64	99.23 12	95.13 106	95.51 157	97.32 204	85.73 231	98.91 182	97.33 58	89.55 260	96.89 216

HQP_MVS		96.14 121	95.90 115	96.85 171	97.42 188	94.60 202	98.80 95	98.56 122	97.28 21	95.34 158	98.28 131	87.09 208	99.03 168	96.07 97	94.27 198	96.92 208

plane_prior3						94.61 200			97.02 39	95.34 158

DWT-MVSNet_test		94.82 188	94.36 181	96.20 227	97.35 193	90.79 270	98.34 168	96.57 307	92.91 195	95.33 160	96.44 266	82.00 277	99.12 152	94.52 144	95.78 191	98.70 142

Fast-Effi-MVS+		96.28 118	95.70 124	98.03 105	98.29 134	95.97 116	98.58 138	98.25 173	91.74 233	95.29 161	97.23 209	91.03 119	99.15 148	92.90 189	97.96 132	98.97 127

EI-MVSNet		95.96 124	95.83 117	96.36 218	97.93 157	93.70 230	98.12 197	98.27 168	93.70 165	95.07 162	99.02 58	92.23 92	98.54 213	94.68 138	93.46 219	96.84 222

MVSTER		96.06 122	95.72 120	97.08 159	98.23 137	95.93 123	98.73 114	98.27 168	94.86 119	95.07 162	98.09 145	88.21 180	98.54 213	96.59 85	93.46 219	96.79 226

OPM-MVS		95.69 138	95.33 136	96.76 175	96.16 272	94.63 197	98.43 160	98.39 154	96.64 50	95.02 164	98.78 85	85.15 241	99.05 163	95.21 131	94.20 201	96.60 256

Fast-Effi-MVS+-dtu		95.87 128	95.85 116	95.91 236	97.74 167	91.74 260	98.69 121	98.15 193	95.56 81	94.92 165	97.68 181	88.98 149	98.79 197	93.19 177	97.78 140	97.20 197

TESTMET0.1,1		94.18 227	93.69 222	95.63 246	96.92 217	89.12 291	96.91 282	94.78 327	93.17 185	94.88 166	96.45 265	78.52 297	98.92 181	93.09 179	98.50 113	98.85 134

VPNet		94.99 176	94.19 189	97.40 144	97.16 206	96.57 93	98.71 117	98.97 29	95.67 76	94.84 167	98.24 137	80.36 290	98.67 203	96.46 90	87.32 291	96.96 205

1112_ss		96.63 102	96.00 113	98.50 75	98.56 123	96.37 101	98.18 192	98.10 208	92.92 194	94.84 167	98.43 115	92.14 95	99.58 112	94.35 148	96.51 163	99.56 66

test-LLR		95.10 173	94.87 161	95.80 241	96.77 225	89.70 283	96.91 282	95.21 322	95.11 107	94.83 169	95.72 287	87.71 196	98.97 172	93.06 180	98.50 113	98.72 140

test-mter		94.08 233	93.51 233	95.80 241	96.77 225	89.70 283	96.91 282	95.21 322	92.89 196	94.83 169	95.72 287	77.69 301	98.97 172	93.06 180	98.50 113	98.72 140

Test_1112_low_res		96.34 114	95.66 127	98.36 85	98.56 123	95.94 120	97.71 238	98.07 213	92.10 225	94.79 171	97.29 206	91.75 103	99.56 115	94.17 153	96.50 164	99.58 64

GA-MVS		94.81 189	94.03 199	97.14 154	97.15 207	93.86 223	96.76 291	97.58 234	94.00 145	94.76 172	97.04 227	80.91 283	98.48 224	91.79 217	96.25 180	99.09 116

BH-untuned		95.95 125	95.72 120	96.65 188	98.55 125	92.26 250	98.23 181	97.79 226	93.73 161	94.62 173	98.01 151	88.97 150	99.00 171	93.04 182	98.51 112	98.68 144

test_djsdf		96.00 123	95.69 125	96.93 168	95.72 289	95.49 142	99.47 2	98.40 152	94.98 113	94.58 174	97.86 162	89.16 143	98.41 244	96.91 69	94.12 206	96.88 218

cascas		94.63 203	93.86 210	96.93 168	96.91 219	94.27 214	96.00 307	98.51 132	85.55 310	94.54 175	96.23 272	84.20 263	98.87 188	95.80 109	96.98 153	97.66 185

DP-MVS		96.59 105	95.93 114	98.57 69	99.34 40	96.19 108	98.70 120	98.39 154	89.45 286	94.52 176	99.35 18	91.85 102	99.85 40	92.89 191	98.88 95	99.68 42

gg-mvs-nofinetune		92.21 261	90.58 275	97.13 155	96.75 228	95.09 156	95.85 309	89.40 342	85.43 311	94.50 177	81.98 333	80.80 286	98.40 250	92.16 205	98.33 121	97.88 176

mvs_anonymous		96.70 101	96.53 97	97.18 152	98.19 141	93.78 225	98.31 174	98.19 182	94.01 144	94.47 178	98.27 134	92.08 98	98.46 229	97.39 56	97.91 133	99.31 92

v1neww		94.83 185	94.22 185	96.68 183	96.39 245	94.85 171	98.87 74	98.11 203	92.45 211	94.45 179	97.06 222	88.82 159	98.54 213	92.93 186	88.91 270	96.65 249

v7new		94.83 185	94.22 185	96.68 183	96.39 245	94.85 171	98.87 74	98.11 203	92.45 211	94.45 179	97.06 222	88.82 159	98.54 213	92.93 186	88.91 270	96.65 249

HQP-NCC					97.20 202		98.05 204		96.43 54	94.45 179

ACMP_Plane					97.20 202		98.05 204		96.43 54	94.45 179

HQP4-MVS										94.45 179			98.96 175			96.87 219

HQP-MVS		95.72 134	95.40 129	96.69 180	97.20 202	94.25 215	98.05 204	98.46 142	96.43 54	94.45 179	97.73 174	86.75 214	98.96 175	95.30 125	94.18 202	96.86 221

v6		94.83 185	94.21 187	96.69 180	96.36 249	94.85 171	98.87 74	98.11 203	92.46 206	94.44 185	97.05 226	88.76 165	98.57 211	92.95 185	88.92 269	96.65 249

MSDG		95.93 126	95.30 139	97.83 113	98.90 96	95.36 146	96.83 290	98.37 157	91.32 248	94.43 186	98.73 91	90.27 129	99.60 106	90.05 249	98.82 100	98.52 152

nrg030		96.28 118	95.72 120	97.96 108	96.90 220	98.15 36	99.39 5	98.31 162	95.47 84	94.42 187	98.35 123	92.09 97	98.69 200	97.50 53	89.05 266	97.04 201

CLD-MVS		95.62 141	95.34 134	96.46 213	97.52 181	93.75 228	97.27 269	98.46 142	95.53 82	94.42 187	98.00 152	86.21 222	98.97 172	96.25 96	94.37 196	96.66 247
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LPG-MVS_test		95.62 141	95.34 134	96.47 210	97.46 184	93.54 231	98.99 57	98.54 125	94.67 123	94.36 189	98.77 87	85.39 236	99.11 156	95.71 113	94.15 204	96.76 229

LGP-MVS_train				96.47 210	97.46 184	93.54 231		98.54 125	94.67 123	94.36 189	98.77 87	85.39 236	99.11 156	95.71 113	94.15 204	96.76 229

v144192		94.39 216	93.70 221	96.48 209	96.06 275	94.35 211	98.58 138	98.16 192	91.45 239	94.33 191	97.02 229	87.50 204	98.45 231	91.08 231	89.11 265	96.63 252

v1		94.75 193	94.11 196	96.69 180	96.27 264	94.87 169	98.69 121	98.12 198	92.43 214	94.32 192	96.94 238	88.71 168	98.54 213	92.66 195	88.84 275	96.67 244

v1141		94.75 193	94.11 196	96.67 186	96.27 264	94.86 170	98.69 121	98.12 198	92.43 214	94.31 193	96.94 238	88.78 164	98.48 224	92.63 196	88.85 274	96.67 244

V42		94.78 190	94.14 192	96.70 179	96.33 256	95.22 152	98.97 61	98.09 211	92.32 221	94.31 193	97.06 222	88.39 177	98.55 212	92.90 189	88.87 272	96.34 276

v7		94.69 197	94.04 198	96.62 193	96.41 244	94.79 190	98.78 102	98.13 196	91.89 229	94.30 195	97.16 212	88.13 184	98.45 231	91.96 214	89.65 257	96.61 254

divwei89l23v2f112		94.76 191	94.12 195	96.67 186	96.28 262	94.85 171	98.69 121	98.12 198	92.44 213	94.29 196	96.94 238	88.85 156	98.48 224	92.67 194	88.79 276	96.67 244

ACMM	93.85 9	95.69 138	95.38 133	96.61 194	97.61 173	93.84 224	98.91 66	98.44 146	95.25 101	94.28 197	98.47 113	86.04 228	99.12 152	95.50 120	93.95 211	96.87 219
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS-LS		95.46 150	95.21 142	96.22 226	98.12 146	93.72 229	98.32 173	98.13 196	93.71 163	94.26 198	97.31 205	92.24 91	98.10 266	94.63 139	90.12 252	96.84 222
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		94.20 224	93.47 235	96.40 216	95.98 278	94.08 218	98.52 148	98.15 193	91.33 247	94.25 199	97.20 211	86.41 219	98.42 237	90.04 250	89.39 263	96.69 243

BH-w/o		95.38 157	95.08 147	96.26 225	98.34 131	91.79 257	97.70 239	97.43 258	92.87 197	94.24 200	97.22 210	88.66 169	98.84 191	91.55 223	97.70 143	98.16 170

XVG-ACMP-BASELINE		94.54 209	94.14 192	95.75 244	96.55 236	91.65 261	98.11 199	98.44 146	94.96 115	94.22 201	97.90 159	79.18 296	99.11 156	94.05 157	93.85 212	96.48 271

v1144		94.59 206	93.92 206	96.60 195	96.21 266	94.78 192	98.59 136	98.14 195	91.86 232	94.21 202	97.02 229	87.97 187	98.41 244	91.72 220	89.57 258	96.61 254

v1192		94.32 218	93.58 228	96.53 205	96.10 273	94.45 206	98.50 153	98.17 190	91.54 237	94.19 203	97.06 222	86.95 212	98.43 236	90.14 245	89.57 258	96.70 238

PAPM		94.95 180	94.00 201	97.78 117	97.04 211	95.65 135	96.03 306	98.25 173	91.23 253	94.19 203	97.80 171	91.27 114	98.86 190	82.61 309	97.61 144	98.84 136

tpmp4_e23		93.91 239	93.42 238	95.38 260	97.62 172	88.59 300	97.52 251	97.34 265	87.94 297	94.17 205	96.79 252	82.91 273	99.05 163	90.62 240	95.91 188	98.50 153

Patchmatch-test		94.42 214	93.68 223	96.63 191	97.60 174	91.76 258	94.83 321	97.49 253	89.45 286	94.14 206	97.10 215	88.99 146	98.83 193	85.37 304	98.13 128	99.29 97

v1240		94.06 235	93.29 239	96.34 221	96.03 277	93.90 222	98.44 158	98.17 190	91.18 255	94.13 207	97.01 231	86.05 226	98.42 237	89.13 267	89.50 261	96.70 238

GBi-Net		94.49 210	93.80 213	96.56 201	98.21 138	95.00 159	98.82 86	98.18 185	92.46 206	94.09 208	97.07 219	81.16 280	97.95 275	92.08 207	92.14 235	96.72 234

test1		94.49 210	93.80 213	96.56 201	98.21 138	95.00 159	98.82 86	98.18 185	92.46 206	94.09 208	97.07 219	81.16 280	97.95 275	92.08 207	92.14 235	96.72 234

FMVSNet3		94.97 179	94.26 184	97.11 157	98.18 143	96.62 90	98.56 143	98.26 172	93.67 170	94.09 208	97.10 215	84.25 260	98.01 272	92.08 207	92.14 235	96.70 238

MIMVSNet		93.26 250	92.21 254	96.41 215	97.73 168	93.13 242	95.65 312	97.03 282	91.27 252	94.04 211	96.06 278	75.33 311	97.19 295	86.56 294	96.23 181	98.92 132

FIs		96.51 108	96.12 109	97.67 125	97.13 208	97.54 59	99.36 8	99.22 14	95.89 69	94.03 212	98.35 123	91.98 100	98.44 234	96.40 93	92.76 231	97.01 202

v2v482		94.69 197	94.03 199	96.65 188	96.17 269	94.79 190	98.67 128	98.08 212	92.72 200	94.00 213	97.16 212	87.69 199	98.45 231	92.91 188	88.87 272	96.72 234

FC-MVSNet-test		96.42 111	96.05 110	97.53 137	96.95 215	97.27 67	99.36 8	99.23 12	95.83 71	93.93 214	98.37 121	92.00 99	98.32 253	96.02 101	92.72 232	97.00 203

UniMVSNet (Re)		95.78 132	95.19 143	97.58 134	96.99 214	97.47 61	98.79 100	99.18 16	95.60 79	93.92 215	97.04 227	91.68 104	98.48 224	95.80 109	87.66 288	96.79 226

UniMVSNet_NR-MVSNet		95.71 136	95.15 144	97.40 144	96.84 223	96.97 77	98.74 111	99.24 10	95.16 105	93.88 216	97.72 177	91.68 104	98.31 255	95.81 107	87.25 293	96.92 208

DU-MVS		95.42 153	94.76 164	97.40 144	96.53 237	96.97 77	98.66 130	98.99 28	95.43 86	93.88 216	97.69 178	88.57 171	98.31 255	95.81 107	87.25 293	96.92 208

Baseline_NR-MVSNet		94.35 217	93.81 212	95.96 234	96.20 267	94.05 219	98.61 135	96.67 304	91.44 240	93.85 218	97.60 186	88.57 171	98.14 264	94.39 146	86.93 296	95.68 292

PS-MVSNAJss		96.43 110	96.26 105	96.92 170	95.84 285	95.08 157	99.16 36	98.50 137	95.87 70	93.84 219	98.34 127	94.51 61	98.61 206	96.88 74	93.45 221	97.06 199

tpmvs		94.60 204	94.36 181	95.33 263	97.46 184	88.60 299	96.88 287	97.68 230	91.29 250	93.80 220	96.42 267	88.58 170	99.24 142	91.06 232	96.04 187	98.17 169

3Dnovator	94.51 5	97.46 67	96.93 78	99.07 43	97.78 165	97.64 54	99.35 10	99.06 21	97.02 39	93.75 221	99.16 42	89.25 140	99.92 13	97.22 59	99.75 30	99.64 54

ITE_SJBPF				95.44 254	97.42 188	91.32 264		97.50 247	95.09 110	93.59 222	98.35 123	81.70 279	98.88 187	89.71 256	93.39 223	96.12 281

TranMVSNet+NR-MVSNet		95.14 172	94.48 174	97.11 157	96.45 242	96.36 102	99.03 54	99.03 24	95.04 111	93.58 223	97.93 157	88.27 179	98.03 271	94.13 154	86.90 298	96.95 207

COLMAP_ROB	93.27 12	95.33 163	94.87 161	96.71 177	99.29 56	93.24 239	98.58 138	98.11 203	89.92 274	93.57 224	99.10 48	86.37 220	99.79 69	90.78 236	98.10 129	97.09 198
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tpm cat1		93.36 245	92.80 245	95.07 269	97.58 176	87.97 306	96.76 291	97.86 224	82.17 322	93.53 225	96.04 279	86.13 223	99.13 151	89.24 265	95.87 189	98.10 171

AllTest		95.24 167	94.65 168	96.99 162	99.25 65	93.21 240	98.59 136	98.18 185	91.36 244	93.52 226	98.77 87	84.67 247	99.72 86	89.70 257	97.87 135	98.02 173

TestCases				96.99 162	99.25 65	93.21 240		98.18 185	91.36 244	93.52 226	98.77 87	84.67 247	99.72 86	89.70 257	97.87 135	98.02 173

FMVSNet2		94.47 212	93.61 226	97.04 160	98.21 138	96.43 99	98.79 100	98.27 168	92.46 206	93.50 228	97.09 217	81.16 280	98.00 273	91.09 230	91.93 239	96.70 238

v148		94.29 220	93.76 218	95.91 236	96.10 273	92.93 244	98.58 138	97.97 220	92.59 204	93.47 229	96.95 236	88.53 174	98.32 253	92.56 198	87.06 295	96.49 270

pmmvs4		94.69 197	93.99 203	96.81 173	95.74 287	95.94 120	97.40 256	97.67 231	90.42 262	93.37 230	97.59 187	89.08 145	98.20 262	92.97 184	91.67 243	96.30 278

PCF-MVS	93.45 11	94.68 200	93.43 236	98.42 83	98.62 120	96.77 86	95.48 313	98.20 181	84.63 315	93.34 231	98.32 129	88.55 173	99.81 50	84.80 305	98.96 92	98.68 144
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
XXY-MVS		95.20 170	94.45 178	97.46 139	96.75 228	96.56 94	98.86 79	98.65 111	93.30 183	93.27 232	98.27 134	84.85 246	98.87 188	94.82 136	91.26 248	96.96 205

jajsoiax		95.45 151	95.03 148	96.73 176	95.42 298	94.63 197	99.14 38	98.52 130	95.74 73	93.22 233	98.36 122	83.87 269	98.65 204	96.95 68	94.04 207	96.91 213

mvs_tets		95.41 155	95.00 149	96.65 188	95.58 293	94.42 207	99.00 56	98.55 124	95.73 74	93.21 234	98.38 120	83.45 272	98.63 205	97.09 63	94.00 209	96.91 213

anonymousdsp		95.42 153	94.91 159	96.94 167	95.10 302	95.90 127	99.14 38	98.41 150	93.75 158	93.16 235	97.46 193	87.50 204	98.41 244	95.63 117	94.03 208	96.50 269

v8		94.47 212	93.77 216	96.57 200	96.36 249	94.83 182	99.05 51	98.19 182	91.92 228	93.16 235	96.97 234	88.82 159	98.48 224	91.69 221	87.79 286	96.39 273

WR-MVS		95.15 171	94.46 176	97.22 149	96.67 233	96.45 98	98.21 183	98.81 61	94.15 138	93.16 235	97.69 178	87.51 202	98.30 257	95.29 127	88.62 277	96.90 215

EPNet_dtu		95.21 169	94.95 154	95.99 233	96.17 269	90.45 277	98.16 193	97.27 272	96.77 44	93.14 238	98.33 128	90.34 127	98.42 237	85.57 301	98.81 101	99.09 116
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
QAPM		96.29 116	95.40 129	98.96 51	97.85 162	97.60 57	99.23 22	98.93 36	89.76 278	93.11 239	99.02 58	89.11 144	99.93 9	91.99 212	99.62 48	99.34 89

GG-mvs-BLEND				96.59 196	96.34 252	94.98 162	96.51 301	88.58 343		93.10 240	94.34 301	80.34 291	98.05 270	89.53 260	96.99 152	96.74 231

v10		94.29 220	93.55 229	96.51 207	96.39 245	94.80 187	98.99 57	98.19 182	91.35 246	93.02 241	96.99 232	88.09 185	98.41 244	90.50 242	88.41 279	96.33 277

3Dnovator+	94.38 6	97.43 72	96.78 85	99.38 10	97.83 163	98.52 12	99.37 7	98.71 91	97.09 37	92.99 242	99.13 44	89.36 137	99.89 27	96.97 65	99.57 56	99.71 33

Patchmtry		93.22 251	92.35 252	95.84 239	96.77 225	93.09 243	94.66 323	97.56 235	87.37 300	92.90 243	96.24 270	88.15 182	97.90 278	87.37 290	90.10 253	96.53 265

v52		94.18 227	93.52 231	96.13 230	95.95 280	94.29 213	99.23 22	98.21 178	91.42 241	92.84 244	96.89 245	87.85 193	98.53 219	91.51 225	87.81 284	95.57 295

V4		94.18 227	93.52 231	96.13 230	95.89 282	94.31 212	99.23 22	98.22 177	91.42 241	92.82 245	96.89 245	87.93 189	98.52 220	91.51 225	87.81 284	95.58 294

v7n		94.19 225	93.43 236	96.47 210	95.90 281	94.38 210	99.26 17	98.34 160	91.99 227	92.76 246	97.13 214	88.31 178	98.52 220	89.48 262	87.70 287	96.52 266

MVS		94.67 201	93.54 230	98.08 101	96.88 221	96.56 94	98.19 188	98.50 137	78.05 328	92.69 247	98.02 149	91.07 118	99.63 103	90.09 246	98.36 120	98.04 172

DSMNet-mixed		92.52 258	92.58 249	92.33 302	94.15 311	82.65 321	98.30 176	94.26 332	89.08 291	92.65 248	95.73 285	85.01 243	95.76 319	86.24 296	97.76 141	98.59 150

EU-MVSNet		93.66 242	94.14 192	92.25 303	95.96 279	83.38 318	98.52 148	98.12 198	94.69 121	92.61 249	98.13 143	87.36 206	96.39 316	91.82 216	90.00 254	96.98 204

semantic-postprocess				94.85 274	97.98 156	90.56 276		98.11 203	93.75 158	92.58 250	97.48 192	83.91 267	97.41 292	92.48 202	91.30 246	96.58 258

pmmvs5		93.65 244	92.97 243	95.68 245	95.49 296	92.37 249	98.20 184	97.28 271	89.66 282	92.58 250	97.26 207	82.14 276	98.09 268	93.18 178	90.95 249	96.58 258

WR-MVS_H		95.05 174	94.46 176	96.81 173	96.86 222	95.82 131	99.24 20	99.24 10	93.87 153	92.53 252	96.84 250	90.37 126	98.24 261	93.24 175	87.93 283	96.38 274

ACMP	93.49 10	95.34 162	94.98 151	96.43 214	97.67 169	93.48 233	98.73 114	98.44 146	94.94 118	92.53 252	98.53 107	84.50 255	99.14 150	95.48 121	94.00 209	96.66 247
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test0.0.03 1		94.08 233	93.51 233	95.80 241	95.53 295	92.89 245	97.38 258	95.97 314	95.11 107	92.51 254	96.66 256	87.71 196	96.94 298	87.03 292	93.67 214	97.57 186

IB-MVS	91.98 17	93.27 249	91.97 256	97.19 151	97.47 183	93.41 236	97.09 276	95.99 313	93.32 181	92.47 255	95.73 285	78.06 299	99.53 122	94.59 142	82.98 310	98.62 149
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
IterMVS		94.09 232	93.85 211	94.80 277	97.99 154	90.35 278	97.18 273	98.12 198	93.68 168	92.46 256	97.34 202	84.05 265	97.41 292	92.51 201	91.33 245	96.62 253
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CP-MVSNet		94.94 182	94.30 183	96.83 172	96.72 230	95.56 139	99.11 44	98.95 33	93.89 151	92.42 257	97.90 159	87.19 207	98.12 265	94.32 149	88.21 280	96.82 225

PS-CasMVS		94.67 201	93.99 203	96.71 177	96.68 232	95.26 151	99.13 41	99.03 24	93.68 168	92.33 258	97.95 155	85.35 238	98.10 266	93.59 168	88.16 282	96.79 226

FMVSNet1		93.19 252	92.07 255	96.56 201	97.54 179	95.00 159	98.82 86	98.18 185	90.38 263	92.27 259	97.07 219	73.68 319	97.95 275	89.36 264	91.30 246	96.72 234

PEN-MVS		94.42 214	93.73 220	96.49 208	96.28 262	94.84 180	99.17 35	99.00 26	93.51 174	92.23 260	97.83 168	86.10 225	97.90 278	92.55 199	86.92 297	96.74 231

v748		93.75 241	93.06 241	95.82 240	95.73 288	92.64 247	99.25 19	98.24 175	91.60 236	92.22 261	96.52 263	87.60 201	98.46 229	90.64 239	85.72 305	96.36 275

OurMVSNet-221017-0		94.21 223	94.00 201	94.85 274	95.60 292	89.22 290	98.89 71	97.43 258	95.29 99	92.18 262	98.52 110	82.86 274	98.59 209	93.46 170	91.76 242	96.74 231

MS-PatchMatch		93.84 240	93.63 224	94.46 286	96.18 268	89.45 286	97.76 235	98.27 168	92.23 224	92.13 263	97.49 191	79.50 293	98.69 200	89.75 255	99.38 80	95.25 297

1314		96.25 120	95.73 119	97.79 116	97.13 208	95.55 141	98.19 188	98.59 115	93.47 176	92.03 264	97.82 169	91.33 113	99.49 125	94.62 140	98.44 116	98.32 167

DTE-MVSNet		93.98 237	93.26 240	96.14 229	96.06 275	94.39 209	99.20 32	98.86 52	93.06 188	91.78 265	97.81 170	85.87 229	97.58 288	90.53 241	86.17 302	96.46 272

LF4IMVS		93.14 253	92.79 246	94.20 289	95.88 283	88.67 298	97.66 243	97.07 279	93.81 156	91.71 266	97.65 182	77.96 300	98.81 195	91.47 227	91.92 240	95.12 298

testgi		93.06 254	92.45 251	94.88 273	96.43 243	89.90 280	98.75 107	97.54 240	95.60 79	91.63 267	97.91 158	74.46 317	97.02 297	86.10 297	93.67 214	97.72 182

tfpnnormal		93.66 242	92.70 248	96.55 204	96.94 216	95.94 120	98.97 61	99.19 15	91.04 256	91.38 268	97.34 202	84.94 244	98.61 206	85.45 303	89.02 268	95.11 299

LTVRE_ROB	92.95 15	94.60 204	93.90 208	96.68 183	97.41 191	94.42 207	98.52 148	98.59 115	91.69 234	91.21 269	98.35 123	84.87 245	99.04 167	91.06 232	93.44 222	96.60 256
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
OpenMVS	93.04 13	95.83 130	95.00 149	98.32 87	97.18 205	97.32 65	99.21 31	98.97 29	89.96 271	91.14 270	99.05 57	86.64 216	99.92 13	93.38 171	99.47 70	97.73 181

pm-mvs1		93.94 238	93.06 241	96.59 196	96.49 240	95.16 153	98.95 63	98.03 219	92.32 221	91.08 271	97.84 165	84.54 254	98.41 244	92.16 205	86.13 304	96.19 280

LP		91.12 279	89.99 281	94.53 282	96.35 251	88.70 297	93.86 328	97.35 264	84.88 313	90.98 272	94.77 296	84.40 256	97.43 291	75.41 326	91.89 241	97.47 187

MVS-HIRNet		89.46 291	88.40 293	92.64 300	97.58 176	82.15 322	94.16 327	93.05 338	75.73 330	90.90 273	82.52 332	79.42 294	98.33 252	83.53 307	98.68 103	97.43 188

FMVSNet5		91.81 272	90.92 266	94.49 283	97.21 201	92.09 252	98.00 210	97.55 239	89.31 289	90.86 274	95.61 290	74.48 316	95.32 321	85.57 301	89.70 256	96.07 283

USDC		93.33 248	92.71 247	95.21 264	96.83 224	90.83 269	96.91 282	97.50 247	93.84 154	90.72 275	98.14 142	77.69 301	98.82 194	89.51 261	93.21 228	95.97 285

MVP-Stereo		94.28 222	93.92 206	95.35 262	94.95 304	92.60 248	97.97 212	97.65 232	91.61 235	90.68 276	97.09 217	86.32 221	98.42 237	89.70 257	99.34 82	95.02 302
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMH+	92.99 14	94.30 219	93.77 216	95.88 238	97.81 164	92.04 254	98.71 117	98.37 157	93.99 146	90.60 277	98.47 113	80.86 285	99.05 163	92.75 193	92.40 234	96.55 263

testpf		88.74 294	89.09 287	87.69 312	95.78 286	83.16 320	84.05 339	94.13 335	85.22 312	90.30 278	94.39 300	74.92 314	95.80 318	89.77 253	93.28 227	84.10 334

Anonymous20231206		91.66 274	91.10 262	93.33 296	94.02 313	87.35 310	98.58 138	97.26 273	90.48 259	90.16 279	96.31 268	83.83 270	96.53 314	79.36 316	89.90 255	96.12 281

SixPastTwentyTwo		93.34 247	92.86 244	94.75 278	95.67 290	89.41 288	98.75 107	96.67 304	93.89 151	90.15 280	98.25 136	80.87 284	98.27 260	90.90 235	90.64 250	96.57 260

PVSNet_0	88.72 19	91.28 277	90.03 280	95.00 270	97.99 154	87.29 311	94.84 320	98.50 137	92.06 226	89.86 281	95.19 291	79.81 292	99.39 133	92.27 204	69.79 334	98.33 166

ACMH	92.88 16	94.55 208	93.95 205	96.34 221	97.63 171	93.26 238	98.81 92	98.49 141	93.43 177	89.74 282	98.53 107	81.91 278	99.08 161	93.69 164	93.30 225	96.70 238
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		91.77 273	90.63 274	95.17 266	94.69 309	91.24 266	98.67 128	97.92 222	86.14 305	89.62 283	97.56 190	75.79 310	98.34 251	90.75 237	84.56 309	95.94 286

TinyColmap		92.31 260	91.53 259	94.65 280	96.92 217	89.75 282	96.92 280	96.68 303	90.45 261	89.62 283	97.85 164	76.06 309	98.81 195	86.74 293	92.51 233	95.41 296

TransMVSNet (Re)		92.67 256	91.51 260	96.15 228	96.58 235	94.65 195	98.90 67	96.73 300	90.86 258	89.46 285	97.86 162	85.62 233	98.09 268	86.45 295	81.12 315	95.71 291

testus		88.91 293	89.08 288	88.40 311	91.39 320	76.05 329	96.56 297	96.48 308	89.38 288	89.39 286	95.17 293	70.94 323	93.56 328	77.04 322	95.41 193	95.61 293

NR-MVSNet		94.98 178	94.16 190	97.44 140	96.53 237	97.22 71	98.74 111	98.95 33	94.96 115	89.25 287	97.69 178	89.32 138	98.18 263	94.59 142	87.40 290	96.92 208

test2356		88.68 295	88.61 291	88.87 310	89.90 326	78.23 326	95.11 315	96.66 306	88.66 294	89.06 288	94.33 302	73.14 321	92.56 332	75.56 325	95.11 195	95.81 289

LCM-MVSNet-Re		95.22 168	95.32 137	94.91 271	98.18 143	87.85 308	98.75 107	95.66 318	95.11 107	88.96 289	96.85 249	90.26 130	97.65 285	95.65 116	98.44 116	99.22 104

TDRefinement		91.06 280	89.68 283	95.21 264	85.35 333	91.49 262	98.51 152	97.07 279	91.47 238	88.83 290	97.84 165	77.31 305	99.09 160	92.79 192	77.98 327	95.04 301

N_pmnet		87.12 299	87.77 296	85.17 319	95.46 297	61.92 342	97.37 260	70.66 350	85.83 309	88.73 291	96.04 279	85.33 240	97.76 284	80.02 313	90.48 251	95.84 287

test_0402		91.32 276	90.27 278	94.48 284	96.60 234	91.12 267	98.50 153	97.22 275	86.10 306	88.30 292	96.98 233	77.65 303	97.99 274	78.13 320	92.94 230	94.34 315

test20.03		90.89 282	90.38 276	92.43 301	93.48 314	88.14 305	98.33 169	97.56 235	93.40 178	87.96 293	96.71 255	80.69 287	94.13 325	79.15 317	86.17 302	95.01 303

MIMVSNet1		89.67 290	88.28 295	93.82 292	92.81 318	91.08 268	98.01 208	97.45 256	87.95 296	87.90 294	95.87 283	67.63 329	94.56 324	78.73 319	88.18 281	95.83 288

Patchmatch-RL test		91.49 275	90.85 267	93.41 295	91.37 321	84.40 315	92.81 329	95.93 316	91.87 231	87.25 295	94.87 295	88.99 146	96.53 314	92.54 200	82.00 312	99.30 95

pmmvs3		86.67 300	84.86 302	92.11 304	88.16 328	87.19 312	96.63 294	94.75 328	79.88 326	87.22 296	92.75 319	66.56 330	95.20 322	81.24 312	76.56 330	93.96 321

K. test v3		92.55 257	91.91 258	94.48 284	95.64 291	89.24 289	99.07 50	94.88 326	94.04 143	86.78 297	97.59 187	77.64 304	97.64 286	92.08 207	89.43 262	96.57 260

lessismore_v0				94.45 287	94.93 305	88.44 302		91.03 340		86.77 298	97.64 184	76.23 308	98.42 237	90.31 244	85.64 306	96.51 268

ambc				89.49 309	86.66 332	75.78 330	92.66 330	96.72 301		86.55 299	92.50 321	46.01 339	97.90 278	90.32 243	82.09 311	94.80 304

PM-MVS		87.77 297	86.55 299	91.40 306	91.03 323	83.36 319	96.92 280	95.18 324	91.28 251	86.48 300	93.42 304	53.27 336	96.74 308	89.43 263	81.97 313	94.11 318

DI_MVS_plusplus_test		94.74 195	93.62 225	98.09 100	95.34 299	95.92 124	98.09 202	97.34 265	94.66 125	85.89 301	95.91 281	80.49 289	99.38 134	96.66 83	98.22 124	98.97 127

OpenMVS_ROB	86.42 20	89.00 292	87.43 298	93.69 293	93.08 316	89.42 287	97.91 219	96.89 296	78.58 327	85.86 302	94.69 297	69.48 325	98.29 259	77.13 321	93.29 226	93.36 324

UnsupCasMVSNet_eth		90.99 281	89.92 282	94.19 290	94.08 312	89.83 281	97.13 275	98.67 104	93.69 166	85.83 303	96.19 275	75.15 312	96.74 308	89.14 266	79.41 321	96.00 284

test_normal		94.72 196	93.59 227	98.11 99	95.30 300	95.95 119	97.91 219	97.39 263	94.64 126	85.70 304	95.88 282	80.52 288	99.36 135	96.69 82	98.30 123	99.01 125

new_pmnet		90.06 287	89.00 290	93.22 299	94.18 310	88.32 304	96.42 302	96.89 296	86.19 304	85.67 305	93.62 303	77.18 306	97.10 296	81.61 311	89.29 264	94.23 316

v17		92.08 264	90.94 264	95.48 251	96.34 252	94.83 182	98.81 92	97.52 241	89.95 272	85.32 306	93.24 307	88.91 152	96.91 300	88.76 274	79.63 320	94.71 307

EG-PatchMatch MVS		91.13 278	90.12 279	94.17 291	94.73 308	89.00 294	98.13 196	97.81 225	89.22 290	85.32 306	96.46 264	67.71 328	98.42 237	87.89 288	93.82 213	95.08 300

v18		92.10 263	90.97 263	95.50 249	96.34 252	94.85 171	98.82 86	97.52 241	89.99 270	85.31 308	93.26 306	88.90 153	96.92 299	88.82 273	79.77 319	94.73 305

v16		92.08 264	90.94 264	95.49 250	96.38 248	94.84 180	98.81 92	97.51 244	89.94 273	85.25 309	93.28 305	88.86 154	96.91 300	88.70 275	79.78 318	94.72 306

v15		91.94 266	90.77 268	95.43 256	96.31 260	94.83 182	98.77 103	97.50 247	89.92 274	85.13 310	93.08 310	88.76 165	96.86 302	88.40 278	79.10 322	94.61 311

v11		91.85 271	90.68 273	95.36 261	96.34 252	94.74 194	98.80 95	97.43 258	89.60 284	85.09 311	93.03 312	88.53 174	96.75 307	87.37 290	79.96 317	94.58 313

V14		91.93 267	90.76 269	95.42 259	96.33 256	94.81 186	98.77 103	97.51 244	89.86 276	85.09 311	93.13 308	88.80 163	96.83 304	88.32 279	79.06 324	94.60 312

V9		91.91 268	90.73 270	95.45 253	96.32 259	94.80 187	98.77 103	97.50 247	89.81 277	85.03 313	93.08 310	88.76 165	96.86 302	88.24 280	79.03 325	94.69 308

v12		91.89 269	90.70 271	95.43 256	96.31 260	94.80 187	98.76 106	97.50 247	89.76 278	84.95 314	93.00 313	88.82 159	96.82 306	88.23 281	79.00 326	94.68 310

v13		91.88 270	90.69 272	95.43 256	96.33 256	94.78 192	98.75 107	97.50 247	89.68 281	84.93 315	92.98 314	88.84 157	96.83 304	88.14 282	79.09 323	94.69 308

pmmvs-eth3d		90.36 286	89.05 289	94.32 288	91.10 322	92.12 251	97.63 246	96.95 290	88.86 292	84.91 316	93.13 308	78.32 298	96.74 308	88.70 275	81.81 314	94.09 319

DeepMVS_CX				86.78 315	97.09 210	72.30 335		95.17 325	75.92 329	84.34 317	95.19 291	70.58 324	95.35 320	79.98 315	89.04 267	92.68 325

new-patchmatchnet		88.50 296	87.45 297	91.67 305	90.31 324	85.89 314	97.16 274	97.33 268	89.47 285	83.63 318	92.77 318	76.38 307	95.06 323	82.70 308	77.29 328	94.06 320

UnsupCasMVSNet_bld		87.17 298	85.12 301	93.31 297	91.94 319	88.77 295	94.92 319	98.30 165	84.30 316	82.30 319	90.04 326	63.96 333	97.25 294	85.85 300	74.47 333	93.93 322

Test4		92.21 261	90.34 277	97.82 115	92.83 317	95.87 130	97.94 215	98.05 218	94.50 131	82.12 320	94.48 298	59.54 335	98.54 213	95.39 123	98.22 124	99.06 121

Anonymous20231211		83.69 303	81.50 305	90.26 307	89.23 327	80.10 325	97.97 212	97.06 281	72.79 332	82.05 321	92.57 320	50.28 337	96.32 317	76.15 324	75.38 331	94.37 314

CMPMVS	66.06 21	89.70 289	89.67 284	89.78 308	93.19 315	76.56 328	97.00 277	98.35 159	80.97 324	81.57 322	97.75 173	74.75 315	98.61 206	89.85 252	93.63 216	94.17 317
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test1235678		86.26 301	85.81 300	87.62 313	86.97 331	75.00 333	96.55 299	96.32 311	86.08 307	81.32 323	92.98 314	73.10 322	92.05 333	71.64 329	87.32 291	95.81 289

1111		84.94 302	84.30 303	86.86 314	87.59 329	75.10 331	96.63 294	96.43 309	82.53 319	80.75 324	92.91 316	68.94 326	93.79 326	68.24 332	84.66 308	91.70 326

.test1245		73.05 311	76.31 309	63.27 332	87.59 329	75.10 331	96.63 294	96.43 309	82.53 319	80.75 324	92.91 316	68.94 326	93.79 326	68.24 332	12.72 345	20.91 343

test12356		83.47 304	83.37 304	83.78 320	84.43 334	70.09 338	95.12 314	95.60 319	82.98 317	78.89 326	92.43 323	64.99 331	91.41 335	70.36 330	85.55 307	89.82 328

testing_2		90.61 285	88.50 292	96.95 166	90.08 325	95.57 138	97.69 240	98.06 215	93.02 190	76.55 327	92.48 322	61.18 334	98.44 234	95.45 122	91.98 238	96.84 222

LCM-MVSNet		78.70 306	76.24 310	86.08 316	77.26 343	71.99 336	94.34 325	96.72 301	61.62 336	76.53 328	89.33 327	33.91 346	92.78 331	81.85 310	74.60 332	93.46 323

PMMVS2		77.95 308	75.44 311	85.46 317	82.54 335	74.95 334	94.23 326	93.08 337	72.80 331	74.68 329	87.38 328	36.36 344	91.56 334	73.95 327	63.94 335	89.87 327

Gipuma		78.40 307	76.75 308	83.38 321	95.54 294	80.43 324	79.42 340	97.40 261	64.67 334	73.46 330	80.82 335	45.65 340	93.14 330	66.32 334	87.43 289	76.56 339
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
YYNet1		90.70 284	89.39 285	94.62 281	94.79 307	90.65 274	97.20 271	97.46 254	87.54 299	72.54 331	95.74 284	86.51 217	96.66 312	86.00 298	86.76 300	96.54 264

MDA-MVSNet_test_wron		90.71 283	89.38 286	94.68 279	94.83 306	90.78 271	97.19 272	97.46 254	87.60 298	72.41 332	95.72 287	86.51 217	96.71 311	85.92 299	86.80 299	96.56 262

MDA-MVSNet-bldmvs		89.97 288	88.35 294	94.83 276	95.21 301	91.34 263	97.64 244	97.51 244	88.36 295	71.17 333	96.13 277	79.22 295	96.63 313	83.65 306	86.27 301	96.52 266

testmv		78.74 305	77.35 306	82.89 322	78.16 342	69.30 339	95.87 308	94.65 329	81.11 323	70.98 334	87.11 330	46.31 338	90.42 336	65.28 335	76.72 329	88.95 329

FPMVS		77.62 309	77.14 307	79.05 324	79.25 339	60.97 343	95.79 310	95.94 315	65.96 333	67.93 335	94.40 299	37.73 343	88.88 338	68.83 331	88.46 278	87.29 330

no-one		74.41 310	70.76 312	85.35 318	79.88 338	76.83 327	94.68 322	94.22 333	80.33 325	63.81 336	79.73 336	35.45 345	93.36 329	71.78 328	36.99 342	85.86 333

tmp_tt		68.90 313	66.97 313	74.68 328	50.78 348	59.95 344	87.13 335	83.47 348	38.80 342	62.21 337	96.23 272	64.70 332	76.91 345	88.91 272	30.49 343	87.19 331

E-PMN		64.94 316	64.25 316	67.02 330	82.28 336	59.36 346	91.83 332	85.63 346	52.69 339	60.22 338	77.28 338	41.06 342	80.12 343	46.15 341	41.14 339	61.57 341

EMVS		64.07 317	63.26 318	66.53 331	81.73 337	58.81 347	91.85 331	84.75 347	51.93 341	59.09 339	75.13 339	43.32 341	79.09 344	42.03 342	39.47 340	61.69 340

PNet_i23d		67.70 314	65.07 315	75.60 326	78.61 340	59.61 345	89.14 334	88.24 344	61.83 335	52.37 340	80.89 334	18.91 348	84.91 340	62.70 337	52.93 337	82.28 335

MVE	62.14 22	63.28 319	59.38 319	74.99 327	74.33 344	65.47 341	85.55 337	80.50 349	52.02 340	51.10 341	75.00 340	10.91 353	80.50 342	51.60 340	53.40 336	78.99 337
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		69.08 312	65.37 314	80.22 323	65.99 346	71.96 337	90.91 333	90.09 341	82.62 318	49.93 342	78.39 337	29.36 347	81.75 341	62.49 338	38.52 341	86.95 332

PMVS	61.03 23	65.95 315	63.57 317	73.09 329	57.90 347	51.22 348	85.05 338	93.93 336	54.45 338	44.32 343	83.57 331	13.22 349	89.15 337	58.68 339	81.00 316	78.91 338
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		63.73 318	58.86 320	78.35 325	67.62 345	67.90 340	86.56 336	87.81 345	58.26 337	42.49 344	70.28 341	11.55 351	85.05 339	63.66 336	41.50 338	82.11 336

testmvs		21.48 323	24.95 324	11.09 336	14.89 349	6.47 351	96.56 297	9.87 352	7.55 344	17.93 345	39.02 343	9.43 354	5.90 348	16.56 345	12.72 345	20.91 343

test123		20.95 324	23.72 325	12.64 335	13.54 350	8.19 350	96.55 299	6.13 353	7.48 345	16.74 346	37.98 344	12.97 350	6.05 347	16.69 344	5.43 347	23.68 342

wuyk23d		30.17 321	30.18 323	30.16 334	78.61 340	43.29 349	66.79 341	14.21 351	17.31 343	14.82 347	11.93 347	11.55 351	41.43 346	37.08 343	19.30 344	5.76 345

cdsmvs_eth3d_5k		23.98 322	31.98 322	0.00 337	0.00 351	0.00 352	0.00 342	98.59 115	0.00 346	0.00 348	98.61 100	90.60 124	0.00 349	0.00 346	0.00 348	0.00 346

pcd_1.5k_mvsjas		7.88 326	10.50 327	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	94.51 61	0.00 349	0.00 346	0.00 348	0.00 346

pcd1.5k->3k		39.42 320	41.78 321	32.35 333	96.17 269	0.00 352	0.00 342	98.54 125	0.00 346	0.00 348	0.00 348	87.78 195	0.00 349	0.00 346	93.56 218	97.06 199

sosnet-low-res		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

sosnet		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

uncertanet		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

Regformer		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

ab-mvs-re		8.20 325	10.94 326	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	98.43 115	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

uanet		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

test_part1								98.84 54				97.38 2			99.78 14	99.76 20

test11111								98.84 54

sam_mvs1												89.45 135

sam_mvs												88.99 146

MTGPA								98.74 79

test_post1							96.68 293				30.43 346	87.85 193	98.69 200	92.59 197

test_post											31.83 345	88.83 158	98.91 182

patchmatchnet-post											95.10 294	89.42 136	98.89 186

MTMP								94.14 334

gm-plane-assit					95.88 283	87.47 309			89.74 280		96.94 238		99.19 146	93.32 174

test9_res														96.39 94	99.57 56	99.69 36

agg_prior2														95.87 106	99.57 56	99.68 42

test_prior4						98.01 42	97.86 227

test_prior				99.19 28	99.31 48	98.22 31		98.84 54					99.70 91			99.65 51

新几何2							97.64 244

旧先验1					99.29 56	97.48 60		98.70 93			99.09 52	95.56 36			99.47 70	99.61 57

无先验							97.58 248	98.72 86	91.38 243				99.87 35	93.36 172		99.60 60

原ACMM2							97.67 242

testdata2													99.89 27	91.65 222

segment_acmp												96.85 4

testdata1							97.32 266		96.34 57

plane_prior7					97.42 188	94.63 197

plane_prior6					97.35 193	94.61 200						87.09 208

plane_prior5								98.56 122					99.03 168	96.07 97	94.27 198	96.92 208

plane_prior4											98.28 131

plane_prior2							98.80 95		97.28 21

plane_prior1					97.37 192

plane_prior						94.60 202	98.44 158		96.74 46						94.22 200

n2								0.00 354

nn								0.00 354

door-mid								94.37 331

test11								98.66 107

door								94.64 330

HQP5-MVS						94.25 215

BP-MVS														95.30 125

HQP3-MVS								98.46 142							94.18 202

HQP2-MVS												86.75 214

NP-MVS					97.28 196	94.51 205					97.73 174

ACMMP++_ref															92.97 229

ACMMP++															93.61 217

Test By Simon												94.64 58

High-resolution multi-view benchmark