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.

MVS_0304		97.70 57	97.25 65	99.07 43	98.90 90	97.83 48	98.20 178	98.74 77	97.51 8	98.03 63	99.06 56	86.12 223	99.93 9	99.02 1	99.64 45	99.44 84

APDe-MVS		99.02 1	98.84 1	99.55 1	99.57 23	98.96 2	99.39 5	98.93 35	97.38 17	99.41 3	99.54 1	96.66 5	99.84 42	98.86 2	99.85 2	99.87 1

CANet		98.05 43	97.76 45	98.90 55	98.73 103	97.27 66	98.35 161	98.78 69	97.37 19	97.72 83	98.96 69	91.53 110	99.92 13	98.79 3	99.65 43	99.51 69

Regformer-4		98.64 10	98.53 7	98.99 47	99.43 35	97.37 63	98.40 157	98.79 67	97.46 12	99.09 13	99.31 21	95.86 31	99.80 57	98.64 4	99.76 23	99.79 4

VDD-MVS		95.82 131	95.23 141	97.61 133	98.84 98	93.98 213	98.68 120	97.40 259	95.02 109	97.95 70	99.34 19	74.37 311	99.78 74	98.64 4	96.80 154	99.08 118

EI-MVSNet-Vis-set		98.47 28	98.39 15	98.69 62	99.46 32	96.49 96	98.30 170	98.69 92	97.21 28	98.84 27	99.36 16	95.41 39	99.78 74	98.62 6	99.65 43	99.80 3

Regformer-3		98.59 16	98.50 11	98.86 57	99.43 35	97.05 74	98.40 157	98.68 95	97.43 13	99.06 14	99.31 21	95.80 32	99.77 79	98.62 6	99.76 23	99.78 7

EI-MVSNet-UG-set		98.41 30	98.34 21	98.61 67	99.45 33	96.32 103	98.28 172	98.68 95	97.17 31	98.74 34	99.37 12	95.25 45	99.79 69	98.57 8	99.54 64	99.73 27

CHOSEN 280x420		97.18 84	97.18 69	97.20 145	98.81 99	93.27 230	95.78 305	99.15 17	95.25 98	96.79 121	98.11 144	92.29 88	99.07 157	98.56 9	99.85 2	99.25 100

xiu_mvs_v1_base_debu		97.60 61	97.56 51	97.72 119	98.35 122	95.98 111	97.86 221	98.51 130	97.13 34	99.01 17	98.40 117	91.56 106	99.80 57	98.53 10	98.68 102	97.37 188

xiu_mvs_v1_base		97.60 61	97.56 51	97.72 119	98.35 122	95.98 111	97.86 221	98.51 130	97.13 34	99.01 17	98.40 117	91.56 106	99.80 57	98.53 10	98.68 102	97.37 188

xiu_mvs_v1_base_debi		97.60 61	97.56 51	97.72 119	98.35 122	95.98 111	97.86 221	98.51 130	97.13 34	99.01 17	98.40 117	91.56 106	99.80 57	98.53 10	98.68 102	97.37 188

VNet		97.79 54	97.40 61	98.96 51	98.88 93	97.55 57	98.63 126	98.93 35	96.74 46	99.02 16	98.84 80	90.33 127	99.83 43	98.53 10	96.66 156	99.50 71

MSLP-MVS++		98.56 21	98.57 5	98.55 71	99.26 63	96.80 83	98.71 112	99.05 22	97.28 21	98.84 27	99.28 25	96.47 9	99.40 128	98.52 14	99.70 37	99.47 77

TSAR-MVS + GP.		98.38 32	98.24 31	98.81 58	99.22 71	97.25 69	98.11 193	98.29 165	97.19 30	98.99 20	99.02 58	96.22 11	99.67 96	98.52 14	98.56 110	99.51 69

Regformer-1		98.66 8	98.51 10	99.12 40	99.35 37	97.81 50	98.37 159	98.76 73	97.49 10	99.20 10	99.21 32	96.08 19	99.79 69	98.42 16	99.73 34	99.75 20

DELS-MVS		98.40 31	98.20 34	98.99 47	99.00 86	97.66 52	97.75 230	98.89 43	97.71 6	98.33 53	98.97 65	94.97 52	99.88 34	98.42 16	99.76 23	99.42 85
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
Regformer-2		98.69 7	98.52 8	99.19 28	99.35 37	98.01 41	98.37 159	98.81 59	97.48 11	99.21 9	99.21 32	96.13 16	99.80 57	98.40 18	99.73 34	99.75 20

alignmvs		97.56 65	97.07 74	99.01 46	98.66 110	98.37 20	98.83 79	98.06 213	96.74 46	98.00 68	97.65 182	90.80 121	99.48 126	98.37 19	96.56 160	99.19 104

TSAR-MVS + MP.		98.78 3	98.62 4	99.24 25	99.69 17	98.28 27	99.14 37	98.66 105	96.84 43	99.56 2	99.31 21	96.34 10	99.70 91	98.32 20	99.73 34	99.73 27

DeepPCF-MVS	96.37 2	97.93 48	98.48 13	96.30 217	99.00 86	89.54 278	97.43 249	98.87 48	98.16 2	99.26 6	99.38 11	96.12 17	99.64 100	98.30 21	99.77 17	99.72 30

canonicalmvs		97.67 59	97.23 67	98.98 49	98.70 106	98.38 17	99.34 11	98.39 152	96.76 45	97.67 86	97.40 196	92.26 89	99.49 122	98.28 22	96.28 174	99.08 118

SD-MVS		98.64 10	98.68 3	98.53 73	99.33 42	98.36 21	98.90 64	98.85 52	97.28 21	99.72 1	99.39 7	96.63 7	97.60 281	98.17 23	99.85 2	99.64 53

MP-MVS-pluss		98.31 39	97.92 42	99.49 4	99.72 11	98.88 4	98.43 154	98.78 69	94.10 137	97.69 85	99.42 5	95.25 45	99.92 13	98.09 24	99.80 9	99.67 46
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CNVR-MVS		98.78 3	98.56 6	99.45 8	99.32 45	98.87 5	98.47 150	98.81 59	97.72 4	98.76 33	99.16 42	97.05 2	99.78 74	98.06 25	99.66 42	99.69 35

MVS_111021_HR		98.47 28	98.34 21	98.88 56	99.22 71	97.32 64	97.91 213	99.58 3	97.20 29	98.33 53	99.00 63	95.99 24	99.64 100	98.05 26	99.76 23	99.69 35

VDDNet		95.36 156	94.53 168	97.86 111	98.10 141	95.13 151	98.85 75	97.75 226	90.46 254	98.36 51	99.39 7	73.27 313	99.64 100	97.98 27	96.58 159	98.81 136

MCST-MVS		98.65 9	98.37 17	99.48 5	99.60 22	98.87 5	98.41 156	98.68 95	97.04 38	98.52 44	98.80 84	96.78 4	99.83 43	97.93 28	99.61 48	99.74 25

MPTG		98.55 22	98.25 29	99.46 6	99.76 1	98.64 8	98.55 139	98.74 77	97.27 25	98.02 64	99.39 7	94.81 54	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 8	98.90 64	98.74 77	97.27 25	98.02 64	99.39 7	94.81 54	99.96 1	97.91 29	99.79 10	99.77 14

MVS_111021_LR		98.34 36	98.23 32	98.67 64	99.27 61	96.90 80	97.95 208	99.58 3	97.14 33	98.44 49	99.01 62	95.03 51	99.62 105	97.91 29	99.75 29	99.50 71

ACMMP_Plus		98.61 13	98.30 25	99.55 1	99.62 21	98.95 3	98.82 81	98.81 59	95.80 72	99.16 12	99.47 4	95.37 40	99.92 13	97.89 32	99.75 29	99.79 4

PS-MVSNAJ		97.73 55	97.77 44	97.62 128	98.68 109	95.58 133	97.34 258	98.51 130	97.29 20	98.66 37	97.88 161	94.51 60	99.90 25	97.87 33	99.17 86	97.39 186

XVS		98.70 5	98.49 12	99.34 13	99.70 15	98.35 22	99.29 14	98.88 46	97.40 14	98.46 45	99.20 35	95.90 29	99.89 27	97.85 34	99.74 32	99.78 7

X-MVStestdata		94.06 230	92.30 247	99.34 13	99.70 15	98.35 22	99.29 14	98.88 46	97.40 14	98.46 45	43.50 336	95.90 29	99.89 27	97.85 34	99.74 32	99.78 7

xiu_mvs_v2_base		97.66 60	97.70 47	97.56 135	98.61 115	95.46 139	97.44 247	98.46 140	97.15 32	98.65 38	98.15 141	94.33 66	99.80 57	97.84 36	98.66 106	97.41 184

DeepC-MVS	95.98 3	97.88 49	97.58 50	98.77 59	99.25 64	96.93 78	98.83 79	98.75 76	96.96 41	96.89 114	99.50 3	90.46 124	99.87 35	97.84 36	99.76 23	99.52 66
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HSP-MVS		98.70 5	98.52 8	99.24 25	99.75 3	98.23 28	99.26 17	98.58 118	97.52 7	99.41 3	98.78 85	96.00 23	99.79 69	97.79 38	99.59 52	99.69 35

CP-MVS		98.57 20	98.36 18	99.19 28	99.66 19	97.86 46	99.34 11	98.87 48	95.96 68	98.60 41	99.13 44	96.05 22	99.94 3	97.77 39	99.86 1	99.77 14

SteuartSystems-ACMMP		98.90 2	98.75 2	99.36 12	99.22 71	98.43 16	99.10 44	98.87 48	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.
APD-MVS_3200maxsize		98.53 25	98.33 24	99.15 37	99.50 27	97.92 45	99.15 36	98.81 59	96.24 58	99.20 10	99.37 12	95.30 43	99.80 57	97.73 41	99.67 39	99.72 30

LFMVS		95.86 129	94.98 150	98.47 78	98.87 94	96.32 103	98.84 78	96.02 307	93.40 173	98.62 39	99.20 35	74.99 306	99.63 103	97.72 42	97.20 148	99.46 81

PHI-MVS		98.34 36	98.06 37	99.18 32	99.15 78	98.12 37	99.04 51	99.09 18	93.32 176	98.83 29	99.10 48	96.54 8	99.83 43	97.70 43	99.76 23	99.59 61

HPM-MVS		98.36 34	98.10 36	99.13 38	99.74 7	97.82 49	99.53 1	98.80 66	94.63 124	98.61 40	98.97 65	95.13 49	99.77 79	97.65 44	99.83 7	99.79 4

HFP-MVS		98.63 12	98.40 14	99.32 16	99.72 11	98.29 25	99.23 21	98.96 30	96.10 65	98.94 21	99.17 39	96.06 20	99.92 13	97.62 45	99.78 14	99.75 20

ACMMPR		98.59 16	98.36 18	99.29 18	99.74 7	98.15 35	99.23 21	98.95 32	96.10 65	98.93 25	99.19 38	95.70 33	99.94 3	97.62 45	99.79 10	99.78 7

jason		97.32 79	97.08 73	98.06 104	97.45 181	95.59 132	97.87 220	97.91 221	94.79 117	98.55 43	98.83 81	91.12 114	99.23 138	97.58 47	99.60 49	99.34 88
jason: jason.
lupinMVS		97.44 71	97.22 68	98.12 98	98.07 142	95.76 129	97.68 235	97.76 225	94.50 128	98.79 30	98.61 100	92.34 86	99.30 133	97.58 47	99.59 52	99.31 91

HPM-MVS_fast		98.38 32	98.13 35	99.12 40	99.75 3	97.86 46	99.44 4	98.82 56	94.46 131	98.94 21	99.20 35	95.16 48	99.74 85	97.58 47	99.85 2	99.77 14

region2R		98.61 13	98.38 16	99.29 18	99.74 7	98.16 34	99.23 21	98.93 35	96.15 60	98.94 21	99.17 39	95.91 28	99.94 3	97.55 50	99.79 10	99.78 7

DeepC-MVS_fast	96.70 1	98.55 22	98.34 21	99.18 32	99.25 64	98.04 39	98.50 147	98.78 69	97.72 4	98.92 26	99.28 25	95.27 44	99.82 48	97.55 50	99.77 17	99.69 35
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS++		98.58 18	98.25 29	99.55 1	99.50 27	99.08 1	98.72 111	98.66 105	97.51 8	98.15 55	98.83 81	95.70 33	99.92 13	97.53 52	99.67 39	99.66 48

nrg030		96.28 118	95.72 120	97.96 108	96.90 213	98.15 35	99.39 5	98.31 160	95.47 84	94.42 181	98.35 123	92.09 96	98.69 195	97.50 53	89.05 260	97.04 196

CSCG		97.85 52	97.74 46	98.20 92	99.67 18	95.16 149	99.22 27	99.32 7	93.04 184	97.02 107	98.92 75	95.36 41	99.91 22	97.43 54	99.64 45	99.52 66

mPP-MVS		98.51 26	98.26 28	99.25 24	99.75 3	98.04 39	99.28 16	98.81 59	96.24 58	98.35 52	99.23 29	95.46 38	99.94 3	97.42 55	99.81 8	99.77 14

mvs_anonymous		96.70 101	96.53 97	97.18 147	98.19 135	93.78 218	98.31 168	98.19 180	94.01 140	94.47 172	98.27 134	92.08 97	98.46 223	97.39 56	97.91 132	99.31 91

NCCC		98.61 13	98.35 20	99.38 10	99.28 60	98.61 10	98.45 151	98.76 73	97.82 3	98.45 48	98.93 73	96.65 6	99.83 43	97.38 57	99.41 76	99.71 32

VPA-MVSNet		95.75 133	95.11 145	97.69 123	97.24 192	97.27 66	98.94 61	99.23 12	95.13 103	95.51 151	97.32 202	85.73 229	98.91 177	97.33 58	89.55 254	96.89 211

3Dnovator	94.51 5	97.46 67	96.93 78	99.07 43	97.78 159	97.64 53	99.35 10	99.06 20	97.02 39	93.75 215	99.16 42	89.25 139	99.92 13	97.22 59	99.75 29	99.64 53

#test#		98.54 24	98.27 27	99.32 16	99.72 11	98.29 25	98.98 58	98.96 30	95.65 78	98.94 21	99.17 39	96.06 20	99.92 13	97.21 60	99.78 14	99.75 20

ACMMP		98.23 41	97.95 41	99.09 42	99.74 7	97.62 55	99.03 52	99.41 6	95.98 67	97.60 91	99.36 16	94.45 64	99.93 9	97.14 61	98.85 97	99.70 34
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
PVSNet_Blended_VisFu		97.70 57	97.46 58	98.44 80	99.27 61	95.91 124	98.63 126	99.16 16	94.48 130	97.67 86	98.88 77	92.80 82	99.91 22	97.11 62	99.12 87	99.50 71

mvs_tets		95.41 153	95.00 148	96.65 183	95.58 286	94.42 200	99.00 54	98.55 122	95.73 74	93.21 228	98.38 120	83.45 265	98.63 200	97.09 63	94.00 203	96.91 208

EPNet		97.28 80	96.87 81	98.51 74	94.98 296	96.14 108	98.90 64	97.02 281	98.28 1	95.99 148	99.11 46	91.36 111	99.89 27	96.98 64	99.19 85	99.50 71
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HyFIR lowres test		96.90 95	96.49 98	98.14 95	99.33 42	95.56 135	97.38 252	99.65 2	92.34 214	97.61 90	98.20 139	89.29 138	99.10 154	96.97 65	97.60 144	99.77 14

3Dnovator+	94.38 6	97.43 72	96.78 85	99.38 10	97.83 157	98.52 11	99.37 7	98.71 89	97.09 37	92.99 236	99.13 44	89.36 136	99.89 27	96.97 65	99.57 55	99.71 32

abl_6		98.30 40	98.03 38	99.13 38	99.56 24	97.76 51	99.13 40	98.82 56	96.14 61	99.26 6	99.37 12	93.33 76	99.93 9	96.96 67	99.67 39	99.69 35

jajsoiax		95.45 149	95.03 147	96.73 171	95.42 291	94.63 190	99.14 37	98.52 128	95.74 73	93.22 227	98.36 122	83.87 262	98.65 199	96.95 68	94.04 201	96.91 208

MVSFormer		97.57 64	97.49 56	97.84 112	98.07 142	95.76 129	99.47 2	98.40 150	94.98 110	98.79 30	98.83 81	92.34 86	98.41 238	96.91 69	99.59 52	99.34 88

test_djsdf		96.00 123	95.69 125	96.93 163	95.72 282	95.49 138	99.47 2	98.40 150	94.98 110	94.58 168	97.86 162	89.16 142	98.41 238	96.91 69	94.12 200	96.88 212

test_prior3		98.22 42	97.90 43	99.19 28	99.31 47	98.22 30	97.80 226	98.84 53	96.12 63	97.89 75	98.69 92	95.96 25	99.70 91	96.89 71	99.60 49	99.65 50

test_prior2							97.80 226		96.12 63	97.89 75	98.69 92	95.96 25		96.89 71	99.60 49

EPP-MVSNet		97.46 67	97.28 64	97.99 106	98.64 112	95.38 141	99.33 13	98.31 160	93.61 167	97.19 99	99.07 55	94.05 70	99.23 138	96.89 71	98.43 117	99.37 87

PS-MVSNAJss		96.43 110	96.26 105	96.92 165	95.84 278	95.08 153	99.16 35	98.50 135	95.87 70	93.84 213	98.34 127	94.51 60	98.61 201	96.88 74	93.45 215	97.06 194

PVSNet_BlendedMVS		96.73 100	96.60 93	97.12 151	99.25 64	95.35 144	98.26 174	99.26 8	94.28 133	97.94 71	97.46 193	92.74 83	99.81 50	96.88 74	93.32 218	96.20 273

PVSNet_Blended		97.38 76	97.12 70	98.14 95	99.25 64	95.35 144	97.28 262	99.26 8	93.13 182	97.94 71	98.21 138	92.74 83	99.81 50	96.88 74	99.40 78	99.27 98

Effi-MVS+		97.12 87	96.69 89	98.39 84	98.19 135	96.72 87	97.37 254	98.43 147	93.71 158	97.65 89	98.02 149	92.20 93	99.25 136	96.87 77	97.79 138	99.19 104

CHOSEN 1792x2688		97.12 87	96.80 82	98.08 101	99.30 52	94.56 197	98.05 198	99.71 1	93.57 168	97.09 101	98.91 76	88.17 180	99.89 27	96.87 77	99.56 61	99.81 2

PGM-MVS		98.49 27	98.23 32	99.27 23	99.72 11	98.08 38	98.99 55	99.49 5	95.43 86	99.03 15	99.32 20	95.56 35	99.94 3	96.80 79	99.77 17	99.78 7

XVG-OURS-SEG-HR		96.51 108	96.34 101	97.02 156	98.77 101	93.76 219	97.79 228	98.50 135	95.45 85	96.94 109	99.09 52	87.87 191	99.55 118	96.76 80	95.83 184	97.74 176

MP-MVS		98.33 38	98.01 39	99.28 20	99.75 3	98.18 33	99.22 27	98.79 67	96.13 62	97.92 73	99.23 29	94.54 59	99.94 3	96.74 81	99.78 14	99.73 27

test_normal		94.72 191	93.59 222	98.11 99	95.30 293	95.95 118	97.91 213	97.39 261	94.64 123	85.70 297	95.88 276	80.52 281	99.36 131	96.69 82	98.30 122	99.01 124

DI_MVS_plusplus_test		94.74 190	93.62 220	98.09 100	95.34 292	95.92 122	98.09 196	97.34 263	94.66 122	85.89 294	95.91 275	80.49 282	99.38 130	96.66 83	98.22 123	98.97 126

agg_prior1		97.95 46	97.51 55	99.28 20	99.30 52	98.38 17	97.81 225	98.72 84	93.16 181	97.57 93	98.66 97	96.14 15	99.81 50	96.63 84	99.56 61	99.66 48

train_agg		97.97 44	97.52 54	99.33 15	99.31 47	98.50 12	97.92 210	98.73 82	92.98 187	97.74 81	98.68 94	96.20 12	99.80 57	96.59 85	99.57 55	99.68 41

agg_prior3		97.87 50	97.42 60	99.23 27	99.29 55	98.23 28	97.92 210	98.72 84	92.38 213	97.59 92	98.64 99	96.09 18	99.79 69	96.59 85	99.57 55	99.68 41

MVSTER		96.06 122	95.72 120	97.08 154	98.23 131	95.93 121	98.73 109	98.27 166	94.86 116	95.07 156	98.09 145	88.21 179	98.54 207	96.59 85	93.46 213	96.79 220

UGNet		96.78 99	96.30 103	98.19 94	98.24 130	95.89 126	98.88 70	98.93 35	97.39 16	96.81 119	97.84 165	82.60 268	99.90 25	96.53 88	99.49 67	98.79 137
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
APD-MVS		98.35 35	98.00 40	99.42 9	99.51 26	98.72 7	98.80 90	98.82 56	94.52 127	99.23 8	99.25 28	95.54 37	99.80 57	96.52 89	99.77 17	99.74 25
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
VPNet		94.99 171	94.19 184	97.40 140	97.16 200	96.57 92	98.71 112	98.97 28	95.67 76	94.84 161	98.24 137	80.36 283	98.67 198	96.46 90	87.32 284	96.96 200

sss		97.39 75	96.98 77	98.61 67	98.60 116	96.61 91	98.22 176	98.93 35	93.97 144	98.01 66	98.48 112	91.98 99	99.85 40	96.45 91	98.15 126	99.39 86

MVS_Test		97.28 80	97.00 76	98.13 97	98.33 126	95.97 115	98.74 106	98.07 211	94.27 134	98.44 49	98.07 146	92.48 85	99.26 135	96.43 92	98.19 125	99.16 109

FIs		96.51 108	96.12 109	97.67 125	97.13 202	97.54 58	99.36 8	99.22 14	95.89 69	94.03 206	98.35 123	91.98 99	98.44 228	96.40 93	92.76 225	97.01 197

test9_res														96.39 94	99.57 55	99.69 35

PMMVS		96.60 103	96.33 102	97.41 139	97.90 153	93.93 214	97.35 257	98.41 148	92.84 193	97.76 79	97.45 195	91.10 116	99.20 140	96.26 95	97.91 132	99.11 114

CLD-MVS		95.62 140	95.34 134	96.46 207	97.52 175	93.75 221	97.27 263	98.46 140	95.53 82	94.42 181	98.00 152	86.21 221	98.97 167	96.25 96	94.37 190	96.66 241
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP_MVS		96.14 121	95.90 115	96.85 166	97.42 182	94.60 195	98.80 90	98.56 120	97.28 21	95.34 152	98.28 131	87.09 207	99.03 163	96.07 97	94.27 192	96.92 203

plane_prior5								98.56 120					99.03 163	96.07 97	94.27 192	96.92 203

CPTT-MVS		97.72 56	97.32 63	98.92 53	99.64 20	97.10 73	99.12 42	98.81 59	92.34 214	98.09 58	99.08 54	93.01 80	99.92 13	96.06 99	99.77 17	99.75 20

DP-MVS Recon		97.86 51	97.46 58	99.06 45	99.53 25	98.35 22	98.33 163	98.89 43	92.62 197	98.05 60	98.94 72	95.34 42	99.65 98	96.04 100	99.42 75	99.19 104

FC-MVSNet-test		96.42 111	96.05 110	97.53 136	96.95 209	97.27 66	99.36 8	99.23 12	95.83 71	93.93 208	98.37 121	92.00 98	98.32 247	96.02 101	92.72 226	97.00 198

Vis-MVSNet		97.42 73	97.11 71	98.34 86	98.66 110	96.23 106	99.22 27	99.00 25	96.63 51	98.04 62	99.21 32	88.05 185	99.35 132	96.01 102	99.21 84	99.45 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ab-mvs		96.42 111	95.71 123	98.55 71	98.63 113	96.75 86	97.88 219	98.74 77	93.84 150	96.54 129	98.18 140	85.34 236	99.75 83	95.93 103	96.35 170	99.15 110

WTY-MVS		97.37 77	96.92 79	98.72 61	98.86 95	96.89 82	98.31 168	98.71 89	95.26 97	97.67 86	98.56 106	92.21 92	99.78 74	95.89 104	96.85 153	99.48 76

XVG-OURS		96.55 107	96.41 99	96.99 157	98.75 102	93.76 219	97.50 246	98.52 128	95.67 76	96.83 116	99.30 24	88.95 150	99.53 119	95.88 105	96.26 175	97.69 179

agg_prior2														95.87 106	99.57 55	99.68 41

UniMVSNet_NR-MVSNet		95.71 135	95.15 144	97.40 140	96.84 216	96.97 76	98.74 106	99.24 10	95.16 102	93.88 210	97.72 177	91.68 103	98.31 249	95.81 107	87.25 286	96.92 203

DU-MVS		95.42 151	94.76 162	97.40 140	96.53 230	96.97 76	98.66 124	98.99 27	95.43 86	93.88 210	97.69 178	88.57 170	98.31 249	95.81 107	87.25 286	96.92 203

UniMVSNet (Re)		95.78 132	95.19 143	97.58 134	96.99 208	97.47 60	98.79 95	99.18 15	95.60 79	93.92 209	97.04 225	91.68 103	98.48 218	95.80 109	87.66 281	96.79 220

cascas		94.63 198	93.86 205	96.93 163	96.91 212	94.27 207	96.00 301	98.51 130	85.55 304	94.54 169	96.23 266	84.20 256	98.87 183	95.80 109	96.98 152	97.66 180

Effi-MVS+-dtu		96.29 116	96.56 94	95.51 242	97.89 154	90.22 272	98.80 90	98.10 206	96.57 52	96.45 138	96.66 251	90.81 119	98.91 177	95.72 111	97.99 130	97.40 185

mvs-test1		96.60 103	96.68 91	96.37 211	97.89 154	91.81 249	98.56 137	98.10 206	96.57 52	96.52 131	97.94 156	90.81 119	99.45 127	95.72 111	98.01 129	97.86 175

LPG-MVS_test		95.62 140	95.34 134	96.47 204	97.46 178	93.54 224	98.99 55	98.54 123	94.67 120	94.36 183	98.77 87	85.39 233	99.11 151	95.71 113	94.15 198	96.76 223

LGP-MVS_train				96.47 204	97.46 178	93.54 224		98.54 123	94.67 120	94.36 183	98.77 87	85.39 233	99.11 151	95.71 113	94.15 198	96.76 223

旧先验2							97.57 243		91.30 244	98.67 36			99.80 57	95.70 115

LCM-MVSNet-Re		95.22 163	95.32 137	94.91 265	98.18 137	87.85 301	98.75 102	95.66 313	95.11 104	88.96 282	96.85 244	90.26 129	97.65 279	95.65 116	98.44 115	99.22 103

anonymousdsp		95.42 151	94.91 158	96.94 162	95.10 295	95.90 125	99.14 37	98.41 148	93.75 153	93.16 229	97.46 193	87.50 203	98.41 238	95.63 117	94.03 202	96.50 263

CDPH-MVS		97.94 47	97.49 56	99.28 20	99.47 31	98.44 14	97.91 213	98.67 102	92.57 200	98.77 32	98.85 79	95.93 27	99.72 86	95.56 118	99.69 38	99.68 41

CostFormer		94.95 175	94.73 163	95.60 241	97.28 190	89.06 285	97.53 244	96.89 291	89.66 276	96.82 118	96.72 249	86.05 225	98.95 174	95.53 119	96.13 180	98.79 137

ACMM	93.85 9	95.69 137	95.38 133	96.61 189	97.61 167	93.84 217	98.91 63	98.44 144	95.25 98	94.28 191	98.47 113	86.04 227	99.12 147	95.50 120	93.95 205	96.87 213
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	93.49 10	95.34 158	94.98 150	96.43 208	97.67 163	93.48 226	98.73 109	98.44 144	94.94 115	92.53 246	98.53 107	84.50 251	99.14 145	95.48 121	94.00 203	96.66 241
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
testing_2		90.61 279	88.50 286	96.95 161	90.08 318	95.57 134	97.69 234	98.06 213	93.02 185	76.55 320	92.48 316	61.18 327	98.44 228	95.45 122	91.98 232	96.84 216

Test4		92.21 255	90.34 271	97.82 115	92.83 310	95.87 127	97.94 209	98.05 216	94.50 128	82.12 313	94.48 292	59.54 328	98.54 207	95.39 123	98.22 123	99.06 120

TAMVS		97.02 90	96.79 84	97.70 122	98.06 144	95.31 146	98.52 142	98.31 160	93.95 145	97.05 106	98.61 100	93.49 75	98.52 214	95.33 124	97.81 137	99.29 96

BP-MVS														95.30 125

HQP-MVS		95.72 134	95.40 129	96.69 175	97.20 196	94.25 208	98.05 198	98.46 140	96.43 54	94.45 173	97.73 174	86.75 213	98.96 170	95.30 125	94.18 196	96.86 215

WR-MVS		95.15 166	94.46 171	97.22 144	96.67 226	96.45 97	98.21 177	98.81 59	94.15 135	93.16 229	97.69 178	87.51 201	98.30 251	95.29 127	88.62 270	96.90 210

PatchFormer-LS_test		95.47 147	95.27 140	96.08 226	97.59 169	90.66 266	98.10 195	97.34 263	93.98 143	96.08 144	96.15 270	87.65 199	99.12 147	95.27 128	95.24 188	98.44 156

tpmrst		95.63 139	95.69 125	95.44 248	97.54 173	88.54 294	96.97 272	97.56 233	93.50 170	97.52 95	96.93 240	89.49 133	99.16 142	95.25 129	96.42 165	98.64 147

CDS-MVSNet		96.99 91	96.69 89	97.90 110	98.05 145	95.98 111	98.20 178	98.33 159	93.67 165	96.95 108	98.49 111	93.54 74	98.42 231	95.24 130	97.74 141	99.31 91
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
OPM-MVS		95.69 137	95.33 136	96.76 170	96.16 265	94.63 190	98.43 154	98.39 152	96.64 50	95.02 158	98.78 85	85.15 238	99.05 158	95.21 131	94.20 195	96.60 250

OMC-MVS		97.55 66	97.34 62	98.20 92	99.33 42	95.92 122	98.28 172	98.59 113	95.52 83	97.97 69	99.10 48	93.28 78	99.49 122	95.09 132	98.88 94	99.19 104

CANet_DTU		96.96 92	96.55 95	98.21 91	98.17 139	96.07 110	97.98 205	98.21 176	97.24 27	97.13 100	98.93 73	86.88 212	99.91 22	95.00 133	99.37 80	98.66 145

UA-Net		97.96 45	97.62 48	98.98 49	98.86 95	97.47 60	98.89 68	99.08 19	96.67 49	98.72 35	99.54 1	93.15 79	99.81 50	94.87 134	98.83 98	99.65 50

114514_t		96.93 93	96.27 104	98.92 53	99.50 27	97.63 54	98.85 75	98.90 41	84.80 308	97.77 78	99.11 46	92.84 81	99.66 97	94.85 135	99.77 17	99.47 77

XXY-MVS		95.20 165	94.45 173	97.46 137	96.75 221	96.56 93	98.86 74	98.65 109	93.30 178	93.27 226	98.27 134	84.85 242	98.87 183	94.82 136	91.26 242	96.96 200

MG-MVS		97.81 53	97.60 49	98.44 80	99.12 80	95.97 115	97.75 230	98.78 69	96.89 42	98.46 45	99.22 31	93.90 73	99.68 95	94.81 137	99.52 66	99.67 46

EI-MVSNet		95.96 124	95.83 117	96.36 212	97.93 151	93.70 223	98.12 191	98.27 166	93.70 160	95.07 156	99.02 58	92.23 91	98.54 207	94.68 138	93.46 213	96.84 216

IterMVS-LS		95.46 148	95.21 142	96.22 220	98.12 140	93.72 222	98.32 167	98.13 194	93.71 158	94.26 192	97.31 203	92.24 90	98.10 260	94.63 139	90.12 246	96.84 216
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.25 120	95.73 119	97.79 116	97.13 202	95.55 137	98.19 182	98.59 113	93.47 171	92.03 258	97.82 169	91.33 112	99.49 122	94.62 140	98.44 115	98.32 166

IS-MVSNet		97.22 82	96.88 80	98.25 90	98.85 97	96.36 101	99.19 33	97.97 218	95.39 88	97.23 98	98.99 64	91.11 115	98.93 175	94.60 141	98.59 108	99.47 77

NR-MVSNet		94.98 173	94.16 185	97.44 138	96.53 230	97.22 70	98.74 106	98.95 32	94.96 112	89.25 280	97.69 178	89.32 137	98.18 257	94.59 142	87.40 283	96.92 203

IB-MVS	91.98 17	93.27 243	91.97 250	97.19 146	97.47 177	93.41 229	97.09 270	95.99 308	93.32 176	92.47 249	95.73 279	78.06 292	99.53 119	94.59 142	82.98 303	98.62 148
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
DWT-MVSNet_test		94.82 183	94.36 176	96.20 221	97.35 187	90.79 263	98.34 162	96.57 302	92.91 190	95.33 154	96.44 260	82.00 270	99.12 147	94.52 144	95.78 185	98.70 141

HY-MVS	93.96 8	96.82 98	96.23 107	98.57 69	98.46 121	97.00 75	98.14 188	98.21 176	93.95 145	96.72 122	97.99 153	91.58 105	99.76 81	94.51 145	96.54 161	98.95 130

Baseline_NR-MVSNet		94.35 212	93.81 207	95.96 228	96.20 260	94.05 212	98.61 129	96.67 299	91.44 235	93.85 212	97.60 186	88.57 170	98.14 258	94.39 146	86.93 289	95.68 286

AdaColmap		97.15 86	96.70 88	98.48 77	99.16 76	96.69 88	98.01 202	98.89 43	94.44 132	96.83 116	98.68 94	90.69 122	99.76 81	94.36 147	99.29 83	98.98 125

1112_ss		96.63 102	96.00 113	98.50 75	98.56 117	96.37 100	98.18 186	98.10 206	92.92 189	94.84 161	98.43 115	92.14 94	99.58 112	94.35 148	96.51 162	99.56 65

CP-MVSNet		94.94 177	94.30 178	96.83 167	96.72 223	95.56 135	99.11 43	98.95 32	93.89 147	92.42 251	97.90 159	87.19 206	98.12 259	94.32 149	88.21 273	96.82 219

CNLPA		97.45 70	97.03 75	98.73 60	99.05 81	97.44 62	98.07 197	98.53 126	95.32 95	96.80 120	98.53 107	93.32 77	99.72 86	94.31 150	99.31 82	99.02 121

testdata				98.26 89	99.20 74	95.36 142		98.68 95	91.89 224	98.60 41	99.10 48	94.44 65	99.82 48	94.27 151	99.44 74	99.58 63

PVSNet	91.96 18	96.35 113	96.15 108	96.96 160	99.17 75	92.05 246	96.08 297	98.68 95	93.69 161	97.75 80	97.80 171	88.86 153	99.69 94	94.26 152	99.01 89	99.15 110

Test_1112_low_res		96.34 114	95.66 127	98.36 85	98.56 117	95.94 119	97.71 232	98.07 211	92.10 220	94.79 165	97.29 204	91.75 102	99.56 115	94.17 153	96.50 163	99.58 63

TranMVSNet+NR-MVSNet		95.14 167	94.48 169	97.11 152	96.45 235	96.36 101	99.03 52	99.03 23	95.04 108	93.58 217	97.93 157	88.27 178	98.03 265	94.13 154	86.90 291	96.95 202

API-MVS		97.41 74	97.25 65	97.91 109	98.70 106	96.80 83	98.82 81	98.69 92	94.53 126	98.11 57	98.28 131	94.50 63	99.57 113	94.12 155	99.49 67	97.37 188

PLC	95.07 4	97.20 83	96.78 85	98.44 80	99.29 55	96.31 105	98.14 188	98.76 73	92.41 211	96.39 139	98.31 130	94.92 53	99.78 74	94.06 156	98.77 101	99.23 102
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
XVG-ACMP-BASELINE		94.54 204	94.14 187	95.75 238	96.55 229	91.65 254	98.11 193	98.44 144	94.96 112	94.22 195	97.90 159	79.18 289	99.11 151	94.05 157	93.85 206	96.48 265

F-COLMAP		97.09 89	96.80 82	97.97 107	99.45 33	94.95 160	98.55 139	98.62 111	93.02 185	96.17 143	98.58 105	94.01 71	99.81 50	93.95 158	98.90 93	99.14 112

MDTV_nov1_ep13_2view						84.26 309	96.89 280		90.97 251	97.90 74		89.89 132		93.91 159		99.18 108

diffmvs		96.32 115	95.74 118	98.07 103	98.26 129	96.14 108	98.53 141	98.23 174	90.10 262	96.88 115	97.73 174	90.16 130	99.15 143	93.90 160	97.85 136	98.91 132

原ACMM1				98.65 65	99.32 45	96.62 89		98.67 102	93.27 179	97.81 77	98.97 65	95.18 47	99.83 43	93.84 161	99.46 72	99.50 71

RPSCF		94.87 179	95.40 129	93.26 292	98.89 92	82.06 316	98.33 163	98.06 213	90.30 258	96.56 126	99.26 27	87.09 207	99.49 122	93.82 162	96.32 172	98.24 167

PAPM_NR		97.46 67	97.11 71	98.50 75	99.50 27	96.41 99	98.63 126	98.60 112	95.18 101	97.06 105	98.06 147	94.26 68	99.57 113	93.80 163	98.87 96	99.52 66

ACMH	92.88 16	94.55 203	93.95 200	96.34 215	97.63 165	93.26 231	98.81 87	98.49 139	93.43 172	89.74 275	98.53 107	81.91 271	99.08 156	93.69 164	93.30 219	96.70 232
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MAR-MVS		96.91 94	96.40 100	98.45 79	98.69 108	96.90 80	98.66 124	98.68 95	92.40 212	97.07 104	97.96 154	91.54 109	99.75 83	93.68 165	98.92 92	98.69 142
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
Vis-MVSNet (Re-imp)		96.87 96	96.55 95	97.83 113	98.73 103	95.46 139	99.20 31	98.30 163	94.96 112	96.60 125	98.87 78	90.05 131	98.59 203	93.67 166	98.60 107	99.46 81

LS3D		97.16 85	96.66 92	98.68 63	98.53 120	97.19 71	98.93 62	98.90 41	92.83 194	95.99 148	99.37 12	92.12 95	99.87 35	93.67 166	99.57 55	98.97 126

PS-CasMVS		94.67 196	93.99 198	96.71 172	96.68 225	95.26 147	99.13 40	99.03 23	93.68 163	92.33 252	97.95 155	85.35 235	98.10 260	93.59 168	88.16 275	96.79 220

CVMVSNet		95.43 150	96.04 111	93.57 288	97.93 151	83.62 310	98.12 191	98.59 113	95.68 75	96.56 126	99.02 58	87.51 201	97.51 284	93.56 169	97.44 145	99.60 59

OurMVSNet-221017-0		94.21 218	94.00 196	94.85 268	95.60 285	89.22 283	98.89 68	97.43 256	95.29 96	92.18 256	98.52 110	82.86 267	98.59 203	93.46 170	91.76 236	96.74 225

OpenMVS	93.04 13	95.83 130	95.00 148	98.32 87	97.18 199	97.32 64	99.21 30	98.97 28	89.96 265	91.14 263	99.05 57	86.64 215	99.92 13	93.38 171	99.47 69	97.73 177

无先验							97.58 242	98.72 84	91.38 238				99.87 35	93.36 172		99.60 59

1121		97.37 77	96.77 87	99.16 35	99.34 39	97.99 44	98.19 182	98.68 95	90.14 261	98.01 66	98.97 65	94.80 56	99.87 35	93.36 172	99.46 72	99.61 56

gm-plane-assit					95.88 276	87.47 302			89.74 274		96.94 236		99.19 141	93.32 174

WR-MVS_H		95.05 169	94.46 171	96.81 168	96.86 215	95.82 128	99.24 20	99.24 10	93.87 149	92.53 246	96.84 245	90.37 125	98.24 255	93.24 175	87.93 276	96.38 268

tpm		94.13 226	93.80 208	95.12 261	96.50 232	87.91 300	97.44 247	95.89 312	92.62 197	96.37 140	96.30 263	84.13 257	98.30 251	93.24 175	91.66 238	99.14 112

Fast-Effi-MVS+-dtu		95.87 128	95.85 116	95.91 230	97.74 161	91.74 253	98.69 116	98.15 191	95.56 81	94.92 159	97.68 181	88.98 148	98.79 192	93.19 177	97.78 139	97.20 192

pmmvs5		93.65 238	92.97 238	95.68 239	95.49 289	92.37 242	98.20 178	97.28 269	89.66 276	92.58 244	97.26 205	82.14 269	98.09 262	93.18 178	90.95 243	96.58 252

TESTMET0.1,1		94.18 222	93.69 217	95.63 240	96.92 210	89.12 284	96.91 276	94.78 320	93.17 180	94.88 160	96.45 259	78.52 290	98.92 176	93.09 179	98.50 112	98.85 133

test-LLR		95.10 168	94.87 159	95.80 235	96.77 218	89.70 276	96.91 276	95.21 315	95.11 104	94.83 163	95.72 281	87.71 195	98.97 167	93.06 180	98.50 112	98.72 139

test-mter		94.08 228	93.51 228	95.80 235	96.77 218	89.70 276	96.91 276	95.21 315	92.89 191	94.83 163	95.72 281	77.69 294	98.97 167	93.06 180	98.50 112	98.72 139

BH-untuned		95.95 125	95.72 120	96.65 183	98.55 119	92.26 243	98.23 175	97.79 224	93.73 156	94.62 167	98.01 151	88.97 149	99.00 166	93.04 182	98.51 111	98.68 143

EPMVS		94.99 171	94.48 169	96.52 200	97.22 194	91.75 252	97.23 264	91.66 332	94.11 136	97.28 97	96.81 246	85.70 230	98.84 186	93.04 182	97.28 147	98.97 126

pmmvs4		94.69 192	93.99 198	96.81 168	95.74 280	95.94 119	97.40 250	97.67 229	90.42 256	93.37 224	97.59 187	89.08 144	98.20 256	92.97 184	91.67 237	96.30 272

v6		94.83 180	94.21 182	96.69 175	96.36 242	94.85 164	98.87 71	98.11 201	92.46 201	94.44 179	97.05 224	88.76 164	98.57 205	92.95 185	88.92 262	96.65 243

v1neww		94.83 180	94.22 180	96.68 178	96.39 238	94.85 164	98.87 71	98.11 201	92.45 206	94.45 173	97.06 220	88.82 158	98.54 207	92.93 186	88.91 263	96.65 243

v7new		94.83 180	94.22 180	96.68 178	96.39 238	94.85 164	98.87 71	98.11 201	92.45 206	94.45 173	97.06 220	88.82 158	98.54 207	92.93 186	88.91 263	96.65 243

v2v482		94.69 192	94.03 194	96.65 183	96.17 262	94.79 183	98.67 122	98.08 210	92.72 195	94.00 207	97.16 210	87.69 198	98.45 225	92.91 188	88.87 265	96.72 228

Fast-Effi-MVS+		96.28 118	95.70 124	98.03 105	98.29 128	95.97 115	98.58 132	98.25 171	91.74 228	95.29 155	97.23 207	91.03 118	99.15 143	92.90 189	97.96 131	98.97 126

V42		94.78 185	94.14 187	96.70 174	96.33 249	95.22 148	98.97 59	98.09 209	92.32 216	94.31 187	97.06 220	88.39 176	98.55 206	92.90 189	88.87 265	96.34 270

DP-MVS		96.59 105	95.93 114	98.57 69	99.34 39	96.19 107	98.70 115	98.39 152	89.45 280	94.52 170	99.35 18	91.85 101	99.85 40	92.89 191	98.88 94	99.68 41

TDRefinement		91.06 274	89.68 277	95.21 258	85.35 326	91.49 255	98.51 146	97.07 277	91.47 233	88.83 283	97.84 165	77.31 298	99.09 155	92.79 192	77.98 320	95.04 294

ACMH+	92.99 14	94.30 214	93.77 211	95.88 232	97.81 158	92.04 247	98.71 112	98.37 155	93.99 142	90.60 270	98.47 113	80.86 278	99.05 158	92.75 193	92.40 228	96.55 257

divwei89l23v2f112		94.76 186	94.12 190	96.67 181	96.28 255	94.85 164	98.69 116	98.12 196	92.44 208	94.29 190	96.94 236	88.85 155	98.48 218	92.67 194	88.79 269	96.67 238

v1		94.75 188	94.11 191	96.69 175	96.27 257	94.87 162	98.69 116	98.12 196	92.43 209	94.32 186	96.94 236	88.71 167	98.54 207	92.66 195	88.84 268	96.67 238

v1141		94.75 188	94.11 191	96.67 181	96.27 257	94.86 163	98.69 116	98.12 196	92.43 209	94.31 187	96.94 236	88.78 163	98.48 218	92.63 196	88.85 267	96.67 238

test_post1							96.68 287				30.43 340	87.85 192	98.69 195	92.59 197

v148		94.29 215	93.76 213	95.91 230	96.10 266	92.93 237	98.58 132	97.97 218	92.59 199	93.47 223	96.95 234	88.53 173	98.32 247	92.56 198	87.06 288	96.49 264

PEN-MVS		94.42 209	93.73 215	96.49 202	96.28 255	94.84 173	99.17 34	99.00 25	93.51 169	92.23 254	97.83 168	86.10 224	97.90 272	92.55 199	86.92 290	96.74 225

Patchmatch-RL test		91.49 269	90.85 261	93.41 289	91.37 314	84.40 308	92.81 323	95.93 311	91.87 226	87.25 288	94.87 289	88.99 145	96.53 308	92.54 200	82.00 305	99.30 94

IterMVS		94.09 227	93.85 206	94.80 271	97.99 148	90.35 271	97.18 267	98.12 196	93.68 163	92.46 250	97.34 201	84.05 258	97.41 286	92.51 201	91.33 239	96.62 247
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
semantic-postprocess				94.85 268	97.98 150	90.56 269		98.11 201	93.75 153	92.58 244	97.48 192	83.91 260	97.41 286	92.48 202	91.30 240	96.58 252

tpm2		94.19 220	93.76 213	95.46 246	97.23 193	89.04 286	97.31 261	96.85 294	87.08 295	96.21 142	96.79 247	83.75 264	98.74 194	92.43 203	96.23 177	98.59 149

PVSNet_0	88.72 19	91.28 271	90.03 274	95.00 264	97.99 148	87.29 304	94.84 314	98.50 135	92.06 221	89.86 274	95.19 285	79.81 285	99.39 129	92.27 204	69.79 327	98.33 165

gg-mvs-nofinetune		92.21 255	90.58 269	97.13 150	96.75 221	95.09 152	95.85 303	89.40 335	85.43 305	94.50 171	81.98 327	80.80 279	98.40 244	92.16 205	98.33 120	97.88 174

pm-mvs1		93.94 233	93.06 236	96.59 191	96.49 233	95.16 149	98.95 60	98.03 217	92.32 216	91.08 264	97.84 165	84.54 250	98.41 238	92.16 205	86.13 297	96.19 274

K. test v3		92.55 251	91.91 252	94.48 278	95.64 284	89.24 282	99.07 49	94.88 319	94.04 139	86.78 290	97.59 187	77.64 297	97.64 280	92.08 207	89.43 256	96.57 254

GBi-Net		94.49 205	93.80 208	96.56 196	98.21 132	95.00 155	98.82 81	98.18 183	92.46 201	94.09 202	97.07 217	81.16 273	97.95 269	92.08 207	92.14 229	96.72 228

test1		94.49 205	93.80 208	96.56 196	98.21 132	95.00 155	98.82 81	98.18 183	92.46 201	94.09 202	97.07 217	81.16 273	97.95 269	92.08 207	92.14 229	96.72 228

FMVSNet3		94.97 174	94.26 179	97.11 152	98.18 137	96.62 89	98.56 137	98.26 170	93.67 165	94.09 202	97.10 213	84.25 254	98.01 266	92.08 207	92.14 229	96.70 232

PatchmatchNet		95.71 135	95.52 128	96.29 218	97.58 170	90.72 265	96.84 283	97.52 239	94.06 138	97.08 102	96.96 233	89.24 140	98.90 180	92.03 211	98.37 118	99.26 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
QAPM		96.29 116	95.40 129	98.96 51	97.85 156	97.60 56	99.23 21	98.93 35	89.76 272	93.11 233	99.02 58	89.11 143	99.93 9	91.99 212	99.62 47	99.34 88

新几何1				99.16 35	99.34 39	98.01 41		98.69 92	90.06 263	98.13 56	98.95 71	94.60 58	99.89 27	91.97 213	99.47 69	99.59 61

v7		94.69 192	94.04 193	96.62 188	96.41 237	94.79 183	98.78 97	98.13 194	91.89 224	94.30 189	97.16 210	88.13 183	98.45 225	91.96 214	89.65 251	96.61 248

MDTV_nov1_ep13			95.40 129		97.48 176	88.34 296	96.85 282	97.29 268	93.74 155	97.48 96	97.26 205	89.18 141	99.05 158	91.92 215	97.43 146

EU-MVSNet		93.66 237	94.14 187	92.25 297	95.96 272	83.38 311	98.52 142	98.12 196	94.69 118	92.61 243	98.13 143	87.36 205	96.39 310	91.82 216	90.00 248	96.98 199

GA-MVS		94.81 184	94.03 194	97.14 149	97.15 201	93.86 216	96.76 285	97.58 232	94.00 141	94.76 166	97.04 225	80.91 276	98.48 218	91.79 217	96.25 176	99.09 115

PatchMatch-RL		96.59 105	96.03 112	98.27 88	99.31 47	96.51 95	97.91 213	99.06 20	93.72 157	96.92 112	98.06 147	88.50 175	99.65 98	91.77 218	99.00 90	98.66 145

v1144		94.59 201	93.92 201	96.60 190	96.21 259	94.78 185	98.59 130	98.14 193	91.86 227	94.21 196	97.02 227	87.97 186	98.41 238	91.72 219	89.57 252	96.61 248

v8		94.47 207	93.77 211	96.57 195	96.36 242	94.83 175	99.05 50	98.19 180	91.92 223	93.16 229	96.97 232	88.82 158	98.48 218	91.69 220	87.79 279	96.39 267

testdata2													99.89 27	91.65 221

BH-w/o		95.38 154	95.08 146	96.26 219	98.34 125	91.79 250	97.70 233	97.43 256	92.87 192	94.24 194	97.22 208	88.66 168	98.84 186	91.55 222	97.70 142	98.16 169

v52		94.18 222	93.52 226	96.13 224	95.95 273	94.29 206	99.23 21	98.21 176	91.42 236	92.84 238	96.89 242	87.85 192	98.53 213	91.51 223	87.81 277	95.57 289

V4		94.18 222	93.52 226	96.13 224	95.89 275	94.31 205	99.23 21	98.22 175	91.42 236	92.82 239	96.89 242	87.93 188	98.52 214	91.51 223	87.81 277	95.58 288

LF4IMVS		93.14 247	92.79 241	94.20 283	95.88 276	88.67 291	97.66 237	97.07 277	93.81 152	91.71 260	97.65 182	77.96 293	98.81 190	91.47 225	91.92 234	95.12 292

JIA-IIPM		93.35 240	92.49 244	95.92 229	96.48 234	90.65 267	95.01 310	96.96 287	85.93 302	96.08 144	87.33 323	87.70 197	98.78 193	91.35 226	95.58 186	98.34 164

Patchmatch-test1		95.32 160	94.97 152	96.35 213	97.67 163	91.29 258	97.33 259	97.60 231	94.68 119	96.92 112	96.95 234	83.97 259	98.50 217	91.33 227	98.32 121	99.25 100

FMVSNet2		94.47 207	93.61 221	97.04 155	98.21 132	96.43 98	98.79 95	98.27 166	92.46 201	93.50 222	97.09 215	81.16 273	98.00 267	91.09 228	91.93 233	96.70 232

v144192		94.39 211	93.70 216	96.48 203	96.06 268	94.35 204	98.58 132	98.16 190	91.45 234	94.33 185	97.02 227	87.50 203	98.45 225	91.08 229	89.11 259	96.63 246

tpmvs		94.60 199	94.36 176	95.33 257	97.46 178	88.60 292	96.88 281	97.68 228	91.29 245	93.80 214	96.42 261	88.58 169	99.24 137	91.06 230	96.04 181	98.17 168

LTVRE_ROB	92.95 15	94.60 199	93.90 203	96.68 178	97.41 185	94.42 200	98.52 142	98.59 113	91.69 229	91.21 262	98.35 123	84.87 241	99.04 162	91.06 230	93.44 216	96.60 250
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
PAPR		96.84 97	96.24 106	98.65 65	98.72 105	96.92 79	97.36 256	98.57 119	93.33 175	96.67 123	97.57 189	94.30 67	99.56 115	91.05 232	98.59 108	99.47 77

SixPastTwentyTwo		93.34 241	92.86 239	94.75 272	95.67 283	89.41 281	98.75 102	96.67 299	93.89 147	90.15 273	98.25 136	80.87 277	98.27 254	90.90 233	90.64 244	96.57 254

COLMAP_ROB	93.27 12	95.33 159	94.87 159	96.71 172	99.29 55	93.24 232	98.58 132	98.11 201	89.92 268	93.57 218	99.10 48	86.37 219	99.79 69	90.78 234	98.10 128	97.09 193
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
pmmvs6		91.77 267	90.63 268	95.17 260	94.69 302	91.24 259	98.67 122	97.92 220	86.14 299	89.62 276	97.56 190	75.79 303	98.34 245	90.75 235	84.56 302	95.94 280

BH-RMVSNet		95.92 127	95.32 137	97.69 123	98.32 127	94.64 189	98.19 182	97.45 254	94.56 125	96.03 146	98.61 100	85.02 239	99.12 147	90.68 236	99.06 88	99.30 94

v748		93.75 236	93.06 236	95.82 234	95.73 281	92.64 240	99.25 19	98.24 173	91.60 231	92.22 255	96.52 257	87.60 200	98.46 223	90.64 237	85.72 298	96.36 269

tpmp4_e23		93.91 234	93.42 233	95.38 254	97.62 166	88.59 293	97.52 245	97.34 263	87.94 291	94.17 199	96.79 247	82.91 266	99.05 158	90.62 238	95.91 182	98.50 152

DTE-MVSNet		93.98 232	93.26 235	96.14 223	96.06 268	94.39 202	99.20 31	98.86 51	93.06 183	91.78 259	97.81 170	85.87 228	97.58 282	90.53 239	86.17 295	96.46 266

v10		94.29 215	93.55 224	96.51 201	96.39 238	94.80 180	98.99 55	98.19 180	91.35 241	93.02 235	96.99 230	88.09 184	98.41 238	90.50 240	88.41 272	96.33 271

ambc				89.49 303	86.66 325	75.78 323	92.66 324	96.72 296		86.55 292	92.50 315	46.01 332	97.90 272	90.32 241	82.09 304	94.80 297

lessismore_v0				94.45 281	94.93 298	88.44 295		91.03 333		86.77 291	97.64 184	76.23 301	98.42 231	90.31 242	85.64 299	96.51 262

v1192		94.32 213	93.58 223	96.53 199	96.10 266	94.45 199	98.50 147	98.17 188	91.54 232	94.19 197	97.06 220	86.95 211	98.43 230	90.14 243	89.57 252	96.70 232

MVS		94.67 196	93.54 225	98.08 101	96.88 214	96.56 93	98.19 182	98.50 135	78.05 322	92.69 241	98.02 149	91.07 117	99.63 103	90.09 244	98.36 119	98.04 171

ADS-MVSNet2		94.58 202	94.40 175	95.11 262	98.00 146	88.74 289	96.04 298	97.30 267	90.15 259	96.47 136	96.64 253	87.89 189	97.56 283	90.08 245	97.06 149	99.02 121

ADS-MVSNet		95.00 170	94.45 173	96.63 186	98.00 146	91.91 248	96.04 298	97.74 227	90.15 259	96.47 136	96.64 253	87.89 189	98.96 170	90.08 245	97.06 149	99.02 121

MSDG		95.93 126	95.30 139	97.83 113	98.90 90	95.36 142	96.83 284	98.37 155	91.32 243	94.43 180	98.73 91	90.27 128	99.60 106	90.05 247	98.82 99	98.52 151

v1921920		94.20 219	93.47 230	96.40 210	95.98 271	94.08 211	98.52 142	98.15 191	91.33 242	94.25 193	97.20 209	86.41 218	98.42 231	90.04 248	89.39 257	96.69 237

dp		94.15 225	93.90 203	94.90 266	97.31 189	86.82 306	96.97 272	97.19 274	91.22 249	96.02 147	96.61 255	85.51 232	99.02 165	90.00 249	94.30 191	98.85 133

CMPMVS	66.06 21	89.70 283	89.67 278	89.78 302	93.19 308	76.56 321	97.00 271	98.35 157	80.97 318	81.57 315	97.75 173	74.75 308	98.61 201	89.85 250	93.63 210	94.17 310
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testpf		88.74 288	89.09 281	87.69 306	95.78 279	83.16 313	84.05 333	94.13 328	85.22 306	90.30 271	94.39 294	74.92 307	95.80 312	89.77 251	93.28 221	84.10 327

TR-MVS		94.94 177	94.20 183	97.17 148	97.75 160	94.14 210	97.59 241	97.02 281	92.28 218	95.75 150	97.64 184	83.88 261	98.96 170	89.77 251	96.15 179	98.40 157

MS-PatchMatch		93.84 235	93.63 219	94.46 280	96.18 261	89.45 279	97.76 229	98.27 166	92.23 219	92.13 257	97.49 191	79.50 286	98.69 195	89.75 253	99.38 79	95.25 291

ITE_SJBPF				95.44 248	97.42 182	91.32 257		97.50 245	95.09 107	93.59 216	98.35 123	81.70 272	98.88 182	89.71 254	93.39 217	96.12 275

MVP-Stereo		94.28 217	93.92 201	95.35 256	94.95 297	92.60 241	97.97 206	97.65 230	91.61 230	90.68 269	97.09 215	86.32 220	98.42 231	89.70 255	99.34 81	95.02 295
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
AllTest		95.24 162	94.65 164	96.99 157	99.25 64	93.21 233	98.59 130	98.18 183	91.36 239	93.52 220	98.77 87	84.67 243	99.72 86	89.70 255	97.87 134	98.02 172

TestCases				96.99 157	99.25 64	93.21 233		98.18 183	91.36 239	93.52 220	98.77 87	84.67 243	99.72 86	89.70 255	97.87 134	98.02 172

GG-mvs-BLEND				96.59 191	96.34 245	94.98 158	96.51 295	88.58 336		93.10 234	94.34 295	80.34 284	98.05 264	89.53 258	96.99 151	96.74 225

USDC		93.33 242	92.71 242	95.21 258	96.83 217	90.83 262	96.91 276	97.50 245	93.84 150	90.72 268	98.14 142	77.69 294	98.82 189	89.51 259	93.21 222	95.97 279

v7n		94.19 220	93.43 231	96.47 204	95.90 274	94.38 203	99.26 17	98.34 158	91.99 222	92.76 240	97.13 212	88.31 177	98.52 214	89.48 260	87.70 280	96.52 260

PM-MVS		87.77 291	86.55 293	91.40 300	91.03 316	83.36 312	96.92 274	95.18 317	91.28 246	86.48 293	93.42 298	53.27 329	96.74 302	89.43 261	81.97 306	94.11 311

FMVSNet1		93.19 246	92.07 249	96.56 196	97.54 173	95.00 155	98.82 81	98.18 183	90.38 257	92.27 253	97.07 217	73.68 312	97.95 269	89.36 262	91.30 240	96.72 228

tpm cat1		93.36 239	92.80 240	95.07 263	97.58 170	87.97 299	96.76 285	97.86 222	82.17 316	93.53 219	96.04 273	86.13 222	99.13 146	89.24 263	95.87 183	98.10 170

UnsupCasMVSNet_eth		90.99 275	89.92 276	94.19 284	94.08 305	89.83 274	97.13 269	98.67 102	93.69 161	85.83 296	96.19 269	75.15 305	96.74 302	89.14 264	79.41 314	96.00 278

v1240		94.06 230	93.29 234	96.34 215	96.03 270	93.90 215	98.44 152	98.17 188	91.18 250	94.13 201	97.01 229	86.05 225	98.42 231	89.13 265	89.50 255	96.70 232

view600		95.60 142	94.93 154	97.62 128	99.05 81	94.85 164	99.09 45	97.01 283	95.36 90	96.52 131	97.37 197	84.55 246	99.59 107	89.07 266	96.39 166	98.40 157

view800		95.60 142	94.93 154	97.62 128	99.05 81	94.85 164	99.09 45	97.01 283	95.36 90	96.52 131	97.37 197	84.55 246	99.59 107	89.07 266	96.39 166	98.40 157

conf0.05thres1000		95.60 142	94.93 154	97.62 128	99.05 81	94.85 164	99.09 45	97.01 283	95.36 90	96.52 131	97.37 197	84.55 246	99.59 107	89.07 266	96.39 166	98.40 157

tfpn		95.60 142	94.93 154	97.62 128	99.05 81	94.85 164	99.09 45	97.01 283	95.36 90	96.52 131	97.37 197	84.55 246	99.59 107	89.07 266	96.39 166	98.40 157

tmp_tt		68.90 307	66.97 307	74.68 322	50.78 341	59.95 337	87.13 329	83.47 341	38.80 336	62.21 330	96.23 266	64.70 325	76.91 339	88.91 270	30.49 336	87.19 324

v18		92.10 257	90.97 257	95.50 243	96.34 245	94.85 164	98.82 81	97.52 239	89.99 264	85.31 301	93.26 300	88.90 152	96.92 293	88.82 271	79.77 312	94.73 298

v17		92.08 258	90.94 258	95.48 245	96.34 245	94.83 175	98.81 87	97.52 239	89.95 266	85.32 299	93.24 301	88.91 151	96.91 294	88.76 272	79.63 313	94.71 300

pmmvs-eth3d		90.36 280	89.05 283	94.32 282	91.10 315	92.12 244	97.63 240	96.95 288	88.86 286	84.91 309	93.13 302	78.32 291	96.74 302	88.70 273	81.81 307	94.09 312

v16		92.08 258	90.94 258	95.49 244	96.38 241	94.84 173	98.81 87	97.51 242	89.94 267	85.25 302	93.28 299	88.86 153	96.91 294	88.70 273	79.78 311	94.72 299

thres600view7		95.49 146	94.77 161	97.67 125	98.98 88	95.02 154	98.85 75	96.90 290	95.38 89	96.63 124	96.90 241	84.29 253	99.59 107	88.65 275	96.33 171	98.40 157

v15		91.94 260	90.77 262	95.43 250	96.31 253	94.83 175	98.77 98	97.50 245	89.92 268	85.13 303	93.08 304	88.76 164	96.86 296	88.40 276	79.10 315	94.61 304

V14		91.93 261	90.76 263	95.42 253	96.33 249	94.81 179	98.77 98	97.51 242	89.86 270	85.09 304	93.13 302	88.80 162	96.83 298	88.32 277	79.06 317	94.60 305

V9		91.91 262	90.73 264	95.45 247	96.32 252	94.80 180	98.77 98	97.50 245	89.81 271	85.03 306	93.08 304	88.76 164	96.86 296	88.24 278	79.03 318	94.69 301

v12		91.89 263	90.70 265	95.43 250	96.31 253	94.80 180	98.76 101	97.50 245	89.76 272	84.95 307	93.00 307	88.82 158	96.82 300	88.23 279	79.00 319	94.68 303

v13		91.88 264	90.69 266	95.43 250	96.33 249	94.78 185	98.75 102	97.50 245	89.68 275	84.93 308	92.98 308	88.84 156	96.83 298	88.14 280	79.09 316	94.69 301

thres400		95.38 154	94.62 165	97.65 127	98.94 89	94.98 158	98.68 120	96.93 289	95.33 94	96.55 128	96.53 256	84.23 255	99.56 115	88.11 281	96.29 173	98.40 157

thres200		95.25 161	94.57 166	97.28 143	98.81 99	94.92 161	98.20 178	97.11 275	95.24 100	96.54 129	96.22 268	84.58 245	99.53 119	87.93 282	96.50 163	97.39 186

EG-PatchMatch MVS		91.13 272	90.12 273	94.17 285	94.73 301	89.00 287	98.13 190	97.81 223	89.22 284	85.32 299	96.46 258	67.71 321	98.42 231	87.89 283	93.82 207	95.08 293

CR-MVSNet		94.76 186	94.15 186	96.59 191	97.00 206	93.43 227	94.96 311	97.56 233	92.46 201	96.93 110	96.24 264	88.15 181	97.88 276	87.38 284	96.65 157	98.46 154

v11		91.85 265	90.68 267	95.36 255	96.34 245	94.74 187	98.80 90	97.43 256	89.60 278	85.09 304	93.03 306	88.53 173	96.75 301	87.37 285	79.96 310	94.58 306

Patchmtry		93.22 245	92.35 246	95.84 233	96.77 218	93.09 236	94.66 317	97.56 233	87.37 294	92.90 237	96.24 264	88.15 181	97.90 272	87.37 285	90.10 247	96.53 259

test0.0.03 1		94.08 228	93.51 228	95.80 235	95.53 288	92.89 238	97.38 252	95.97 309	95.11 104	92.51 248	96.66 251	87.71 195	96.94 292	87.03 287	93.67 208	97.57 181

TinyColmap		92.31 254	91.53 253	94.65 274	96.92 210	89.75 275	96.92 274	96.68 298	90.45 255	89.62 276	97.85 164	76.06 302	98.81 190	86.74 288	92.51 227	95.41 290

MIMVSNet		93.26 244	92.21 248	96.41 209	97.73 162	93.13 235	95.65 306	97.03 280	91.27 247	94.04 205	96.06 272	75.33 304	97.19 289	86.56 289	96.23 177	98.92 131

TransMVSNet (Re)		92.67 250	91.51 254	96.15 222	96.58 228	94.65 188	98.90 64	96.73 295	90.86 252	89.46 278	97.86 162	85.62 231	98.09 262	86.45 290	81.12 308	95.71 285

DSMNet-mixed		92.52 252	92.58 243	92.33 296	94.15 304	82.65 314	98.30 170	94.26 325	89.08 285	92.65 242	95.73 279	85.01 240	95.76 313	86.24 291	97.76 140	98.59 149

testgi		93.06 248	92.45 245	94.88 267	96.43 236	89.90 273	98.75 102	97.54 238	95.60 79	91.63 261	97.91 158	74.46 310	97.02 291	86.10 292	93.67 208	97.72 178

YYNet1		90.70 278	89.39 279	94.62 275	94.79 300	90.65 267	97.20 265	97.46 252	87.54 293	72.54 324	95.74 278	86.51 216	96.66 306	86.00 293	86.76 293	96.54 258

MDA-MVSNet_test_wron		90.71 277	89.38 280	94.68 273	94.83 299	90.78 264	97.19 266	97.46 252	87.60 292	72.41 325	95.72 281	86.51 216	96.71 305	85.92 294	86.80 292	96.56 256

UnsupCasMVSNet_bld		87.17 292	85.12 295	93.31 291	91.94 312	88.77 288	94.92 313	98.30 163	84.30 310	82.30 312	90.04 320	63.96 326	97.25 288	85.85 295	74.47 326	93.93 315

EPNet_dtu		95.21 164	94.95 153	95.99 227	96.17 262	90.45 270	98.16 187	97.27 270	96.77 44	93.14 232	98.33 128	90.34 126	98.42 231	85.57 296	98.81 100	99.09 115
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet5		91.81 266	90.92 260	94.49 277	97.21 195	92.09 245	98.00 204	97.55 237	89.31 283	90.86 267	95.61 284	74.48 309	95.32 315	85.57 296	89.70 250	96.07 277

Patchmatch-test		94.42 209	93.68 218	96.63 186	97.60 168	91.76 251	94.83 315	97.49 251	89.45 280	94.14 200	97.10 213	88.99 145	98.83 188	85.37 298	98.13 127	99.29 96

PCF-MVS	93.45 11	94.68 195	93.43 231	98.42 83	98.62 114	96.77 85	95.48 307	98.20 179	84.63 309	93.34 225	98.32 129	88.55 172	99.81 50	84.80 299	98.96 91	98.68 143
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MDA-MVSNet-bldmvs		89.97 282	88.35 288	94.83 270	95.21 294	91.34 256	97.64 238	97.51 242	88.36 289	71.17 326	96.13 271	79.22 288	96.63 307	83.65 300	86.27 294	96.52 260

MVS-HIRNet		89.46 285	88.40 287	92.64 294	97.58 170	82.15 315	94.16 321	93.05 331	75.73 324	90.90 266	82.52 326	79.42 287	98.33 246	83.53 301	98.68 102	97.43 183

new-patchmatchnet		88.50 290	87.45 291	91.67 299	90.31 317	85.89 307	97.16 268	97.33 266	89.47 279	83.63 311	92.77 312	76.38 300	95.06 317	82.70 302	77.29 321	94.06 313

PAPM		94.95 175	94.00 196	97.78 117	97.04 205	95.65 131	96.03 300	98.25 171	91.23 248	94.19 197	97.80 171	91.27 113	98.86 185	82.61 303	97.61 143	98.84 135

LCM-MVSNet		78.70 300	76.24 304	86.08 310	77.26 336	71.99 329	94.34 319	96.72 296	61.62 330	76.53 321	89.33 321	33.91 339	92.78 325	81.85 304	74.60 325	93.46 316

new_pmnet		90.06 281	89.00 284	93.22 293	94.18 303	88.32 297	96.42 296	96.89 291	86.19 298	85.67 298	93.62 297	77.18 299	97.10 290	81.61 305	89.29 258	94.23 309

pmmvs3		86.67 294	84.86 296	92.11 298	88.16 321	87.19 305	96.63 288	94.75 321	79.88 320	87.22 289	92.75 313	66.56 323	95.20 316	81.24 306	76.56 323	93.96 314

N_pmnet		87.12 293	87.77 290	85.17 313	95.46 290	61.92 335	97.37 254	70.66 343	85.83 303	88.73 284	96.04 273	85.33 237	97.76 278	80.02 307	90.48 245	95.84 281

TAPA-MVS	93.98 7	95.35 157	94.56 167	97.74 118	99.13 79	94.83 175	98.33 163	98.64 110	86.62 296	96.29 141	98.61 100	94.00 72	99.29 134	80.00 308	99.41 76	99.09 115
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepMVS_CX				86.78 309	97.09 204	72.30 328		95.17 318	75.92 323	84.34 310	95.19 285	70.58 317	95.35 314	79.98 309	89.04 261	92.68 318

Anonymous20231206		91.66 268	91.10 256	93.33 290	94.02 306	87.35 303	98.58 132	97.26 271	90.48 253	90.16 272	96.31 262	83.83 263	96.53 308	79.36 310	89.90 249	96.12 275

test20.03		90.89 276	90.38 270	92.43 295	93.48 307	88.14 298	98.33 163	97.56 233	93.40 173	87.96 286	96.71 250	80.69 280	94.13 319	79.15 311	86.17 295	95.01 296

PatchT		93.06 248	91.97 250	96.35 213	96.69 224	92.67 239	94.48 318	97.08 276	86.62 296	97.08 102	92.23 318	87.94 187	97.90 272	78.89 312	96.69 155	98.49 153

MIMVSNet1		89.67 284	88.28 289	93.82 286	92.81 311	91.08 261	98.01 202	97.45 254	87.95 290	87.90 287	95.87 277	67.63 322	94.56 318	78.73 313	88.18 274	95.83 282

test_0402		91.32 270	90.27 272	94.48 278	96.60 227	91.12 260	98.50 147	97.22 273	86.10 300	88.30 285	96.98 231	77.65 296	97.99 268	78.13 314	92.94 224	94.34 308

OpenMVS_ROB	86.42 20	89.00 286	87.43 292	93.69 287	93.08 309	89.42 280	97.91 213	96.89 291	78.58 321	85.86 295	94.69 291	69.48 318	98.29 253	77.13 315	93.29 220	93.36 317

testus		88.91 287	89.08 282	88.40 305	91.39 313	76.05 322	96.56 291	96.48 303	89.38 282	89.39 279	95.17 287	70.94 316	93.56 322	77.04 316	95.41 187	95.61 287

RPMNet		92.52 252	91.17 255	96.59 191	97.00 206	93.43 227	94.96 311	97.26 271	82.27 315	96.93 110	92.12 319	86.98 210	97.88 276	76.32 317	96.65 157	98.46 154

Anonymous20231211		83.69 297	81.50 299	90.26 301	89.23 320	80.10 318	97.97 206	97.06 279	72.79 326	82.05 314	92.57 314	50.28 330	96.32 311	76.15 318	75.38 324	94.37 307

test2356		88.68 289	88.61 285	88.87 304	89.90 319	78.23 319	95.11 309	96.66 301	88.66 288	89.06 281	94.33 296	73.14 314	92.56 326	75.56 319	95.11 189	95.81 283

LP		91.12 273	89.99 275	94.53 276	96.35 244	88.70 290	93.86 322	97.35 262	84.88 307	90.98 265	94.77 290	84.40 252	97.43 285	75.41 320	91.89 235	97.47 182

PMMVS2		77.95 302	75.44 305	85.46 311	82.54 328	74.95 327	94.23 320	93.08 330	72.80 325	74.68 322	87.38 322	36.36 337	91.56 328	73.95 321	63.94 328	89.87 320

no-one		74.41 304	70.76 306	85.35 312	79.88 331	76.83 320	94.68 316	94.22 326	80.33 319	63.81 329	79.73 330	35.45 338	93.36 323	71.78 322	36.99 335	85.86 326

test1235678		86.26 295	85.81 294	87.62 307	86.97 324	75.00 326	96.55 293	96.32 306	86.08 301	81.32 316	92.98 308	73.10 315	92.05 327	71.64 323	87.32 284	95.81 283

test12356		83.47 298	83.37 298	83.78 314	84.43 327	70.09 331	95.12 308	95.60 314	82.98 311	78.89 319	92.43 317	64.99 324	91.41 329	70.36 324	85.55 300	89.82 321

FPMVS		77.62 303	77.14 301	79.05 318	79.25 332	60.97 336	95.79 304	95.94 310	65.96 327	67.93 328	94.40 293	37.73 336	88.88 332	68.83 325	88.46 271	87.29 323

1111		84.94 296	84.30 297	86.86 308	87.59 322	75.10 324	96.63 288	96.43 304	82.53 313	80.75 317	92.91 310	68.94 319	93.79 320	68.24 326	84.66 301	91.70 319

.test1245		73.05 305	76.31 303	63.27 326	87.59 322	75.10 324	96.63 288	96.43 304	82.53 313	80.75 317	92.91 310	68.94 319	93.79 320	68.24 326	12.72 338	20.91 336

Gipuma		78.40 301	76.75 302	83.38 315	95.54 287	80.43 317	79.42 334	97.40 259	64.67 328	73.46 323	80.82 329	45.65 333	93.14 324	66.32 328	87.43 282	76.56 332
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testmv		78.74 299	77.35 300	82.89 316	78.16 335	69.30 332	95.87 302	94.65 322	81.11 317	70.98 327	87.11 324	46.31 331	90.42 330	65.28 329	76.72 322	88.95 322

wuykxyi23d		63.73 312	58.86 314	78.35 319	67.62 338	67.90 333	86.56 330	87.81 338	58.26 331	42.49 337	70.28 335	11.55 344	85.05 333	63.66 330	41.50 331	82.11 329

PNet_i23d		67.70 308	65.07 309	75.60 320	78.61 333	59.61 338	89.14 328	88.24 337	61.83 329	52.37 333	80.89 328	18.91 341	84.91 334	62.70 331	52.93 330	82.28 328

ANet_high		69.08 306	65.37 308	80.22 317	65.99 339	71.96 330	90.91 327	90.09 334	82.62 312	49.93 335	78.39 331	29.36 340	81.75 335	62.49 332	38.52 334	86.95 325

PMVS	61.03 23	65.95 309	63.57 311	73.09 323	57.90 340	51.22 341	85.05 332	93.93 329	54.45 332	44.32 336	83.57 325	13.22 342	89.15 331	58.68 333	81.00 309	78.91 331
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	62.14 22	63.28 313	59.38 313	74.99 321	74.33 337	65.47 334	85.55 331	80.50 342	52.02 334	51.10 334	75.00 334	10.91 346	80.50 336	51.60 334	53.40 329	78.99 330
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		64.94 310	64.25 310	67.02 324	82.28 329	59.36 339	91.83 326	85.63 339	52.69 333	60.22 331	77.28 332	41.06 335	80.12 337	46.15 335	41.14 332	61.57 334

EMVS		64.07 311	63.26 312	66.53 325	81.73 330	58.81 340	91.85 325	84.75 340	51.93 335	59.09 332	75.13 333	43.32 334	79.09 338	42.03 336	39.47 333	61.69 333

wuyk23d		30.17 315	30.18 317	30.16 328	78.61 333	43.29 342	66.79 335	14.21 344	17.31 337	14.82 340	11.93 341	11.55 344	41.43 340	37.08 337	19.30 337	5.76 338

test123		20.95 318	23.72 319	12.64 329	13.54 343	8.19 343	96.55 293	6.13 346	7.48 339	16.74 339	37.98 338	12.97 343	6.05 341	16.69 338	5.43 340	23.68 335

testmvs		21.48 317	24.95 318	11.09 330	14.89 342	6.47 344	96.56 291	9.87 345	7.55 338	17.93 338	39.02 337	9.43 347	5.90 342	16.56 339	12.72 338	20.91 336

cdsmvs_eth3d_5k		23.98 316	31.98 316	0.00 331	0.00 344	0.00 345	0.00 336	98.59 113	0.00 340	0.00 341	98.61 100	90.60 123	0.00 343	0.00 340	0.00 341	0.00 339

pcd_1.5k_mvsjas		7.88 320	10.50 321	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	94.51 60	0.00 343	0.00 340	0.00 341	0.00 339

pcd1.5k->3k		39.42 314	41.78 315	32.35 327	96.17 262	0.00 345	0.00 336	98.54 123	0.00 340	0.00 341	0.00 342	87.78 194	0.00 343	0.00 340	93.56 212	97.06 194

sosnet-low-res		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

sosnet		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

uncertanet		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

Regformer		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

ab-mvs-re		8.20 319	10.94 320	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	98.43 115	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

uanet		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

ESAPD								98.84 53

sam_mvs1												89.45 134

sam_mvs												88.99 145

MTGPA								98.74 77

test_post											31.83 339	88.83 157	98.91 177

patchmatchnet-post											95.10 288	89.42 135	98.89 181

MTMP								94.14 327

TEST9					99.31 47	98.50 12	97.92 210	98.73 82	92.63 196	97.74 81	98.68 94	96.20 12	99.80 57

test_8					99.29 55	98.44 14	97.89 218	98.72 84	92.98 187	97.70 84	98.66 97	96.20 12	99.80 57

agg_prior					99.30 52	98.38 17		98.72 84		97.57 93			99.81 50

test_prior4						98.01 41	97.86 221

test_prior				99.19 28	99.31 47	98.22 30		98.84 53					99.70 91			99.65 50

新几何2							97.64 238

旧先验1					99.29 55	97.48 59		98.70 91			99.09 52	95.56 35			99.47 69	99.61 56

原ACMM2							97.67 236

test222					99.23 70	97.17 72	97.40 250	98.66 105	88.68 287	98.05 60	98.96 69	94.14 69			99.53 65	99.61 56

segment_acmp												96.85 3

testdata1							97.32 260		96.34 57

test12				99.18 32	99.16 76	98.19 32		98.53 126		98.07 59		95.13 49	99.72 86		99.56 61	99.63 55

plane_prior7					97.42 182	94.63 190

plane_prior6					97.35 187	94.61 193						87.09 207

plane_prior4											98.28 131

plane_prior3						94.61 193			97.02 39	95.34 152

plane_prior2							98.80 90		97.28 21

plane_prior1					97.37 186

plane_prior						94.60 195	98.44 152		96.74 46						94.22 194

n2								0.00 347

nn								0.00 347

door-mid								94.37 324

test11								98.66 105

door								94.64 323

HQP5-MVS						94.25 208

HQP-NCC					97.20 196		98.05 198		96.43 54	94.45 173

ACMP_Plane					97.20 196		98.05 198		96.43 54	94.45 173

HQP4-MVS										94.45 173			98.96 170			96.87 213

HQP3-MVS								98.46 140							94.18 196

HQP2-MVS												86.75 213

NP-MVS					97.28 190	94.51 198					97.73 174

ACMMP++_ref															92.97 223

ACMMP++															93.61 211

Test By Simon												94.64 57

High-resolution multi-view benchmark