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 59	97.25 67	99.07 45	98.90 99	97.83 51	98.20 194	98.74 80	97.51 8	98.03 66	99.06 59	86.12 226	99.93 9	99.02 1	99.64 48	99.44 87

APDe-MVS		99.02 1	98.84 1	99.55 3	99.57 25	98.96 5	99.39 5	98.93 36	97.38 17	99.41 4	99.54 1	96.66 8	99.84 45	98.86 2	99.85 2	99.87 1

CANet		98.05 45	97.76 47	98.90 57	98.73 118	97.27 69	98.35 177	98.78 72	97.37 19	97.72 86	98.96 72	91.53 113	99.92 15	98.79 3	99.65 46	99.51 72

Regformer-4		98.64 11	98.53 7	98.99 49	99.43 38	97.37 66	98.40 173	98.79 70	97.46 12	99.09 16	99.31 22	95.86 34	99.80 60	98.64 4	99.76 26	99.79 4

VDD-MVS		95.82 133	95.23 143	97.61 137	98.84 113	93.98 229	98.68 133	97.40 262	95.02 115	97.95 73	99.34 20	74.37 327	99.78 77	98.64 4	96.80 157	99.08 123

EI-MVSNet-Vis-set		98.47 30	98.39 15	98.69 64	99.46 35	96.49 99	98.30 186	98.69 95	97.21 28	98.84 30	99.36 17	95.41 42	99.78 77	98.62 6	99.65 46	99.80 3

Regformer-3		98.59 18	98.50 11	98.86 59	99.43 38	97.05 77	98.40 173	98.68 98	97.43 13	99.06 17	99.31 22	95.80 35	99.77 82	98.62 6	99.76 26	99.78 7

EI-MVSNet-UG-set		98.41 32	98.34 22	98.61 69	99.45 36	96.32 106	98.28 188	98.68 98	97.17 31	98.74 37	99.37 13	95.25 48	99.79 72	98.57 8	99.54 67	99.73 30

CHOSEN 280x420		97.18 86	97.18 71	97.20 159	98.81 114	93.27 246	95.78 321	99.15 18	95.25 104	96.79 126	98.11 147	92.29 91	99.07 172	98.56 9	99.85 2	99.25 103

xiu_mvs_v1_base_debu		97.60 63	97.56 53	97.72 121	98.35 137	95.98 114	97.86 237	98.51 133	97.13 34	99.01 20	98.40 120	91.56 109	99.80 60	98.53 10	98.68 105	97.37 200

xiu_mvs_v1_base		97.60 63	97.56 53	97.72 121	98.35 137	95.98 114	97.86 237	98.51 133	97.13 34	99.01 20	98.40 120	91.56 109	99.80 60	98.53 10	98.68 105	97.37 200

xiu_mvs_v1_base_debi		97.60 63	97.56 53	97.72 121	98.35 137	95.98 114	97.86 237	98.51 133	97.13 34	99.01 20	98.40 120	91.56 109	99.80 60	98.53 10	98.68 105	97.37 200

VNet		97.79 56	97.40 63	98.96 53	98.88 108	97.55 60	98.63 140	98.93 36	96.74 46	99.02 19	98.84 83	90.33 130	99.83 46	98.53 10	96.66 159	99.50 74

MSLP-MVS++		98.56 23	98.57 5	98.55 73	99.26 66	96.80 86	98.71 125	99.05 23	97.28 21	98.84 30	99.28 28	96.47 12	99.40 135	98.52 14	99.70 40	99.47 80

TSAR-MVS + GP.		98.38 34	98.24 33	98.81 60	99.22 74	97.25 72	98.11 209	98.29 168	97.19 30	98.99 23	99.02 61	96.22 14	99.67 99	98.52 14	98.56 113	99.51 72

Regformer-1		98.66 9	98.51 10	99.12 42	99.35 40	97.81 53	98.37 175	98.76 76	97.49 10	99.20 13	99.21 35	96.08 22	99.79 72	98.42 16	99.73 37	99.75 23

DELS-MVS		98.40 33	98.20 36	98.99 49	99.00 89	97.66 55	97.75 246	98.89 44	97.71 6	98.33 56	98.97 68	94.97 55	99.88 37	98.42 16	99.76 26	99.42 88
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 8	98.52 8	99.19 30	99.35 40	98.01 44	98.37 175	98.81 62	97.48 11	99.21 12	99.21 35	96.13 19	99.80 60	98.40 18	99.73 37	99.75 23

alignmvs		97.56 67	97.07 76	99.01 48	98.66 125	98.37 23	98.83 92	98.06 216	96.74 46	98.00 71	97.65 185	90.80 124	99.48 133	98.37 19	96.56 163	99.19 109

TSAR-MVS + MP.		98.78 3	98.62 4	99.24 27	99.69 17	98.28 30	99.14 44	98.66 108	96.84 43	99.56 2	99.31 22	96.34 13	99.70 94	98.32 20	99.73 37	99.73 30

DeepPCF-MVS	96.37 2	97.93 50	98.48 13	96.30 232	99.00 89	89.54 294	97.43 265	98.87 49	98.16 2	99.26 9	99.38 12	96.12 20	99.64 103	98.30 21	99.77 20	99.72 33

canonicalmvs		97.67 61	97.23 69	98.98 51	98.70 121	98.38 20	99.34 11	98.39 155	96.76 45	97.67 89	97.40 200	92.26 92	99.49 129	98.28 22	96.28 181	99.08 123

SD-MVS		98.64 11	98.68 3	98.53 75	99.33 45	98.36 24	98.90 74	98.85 53	97.28 21	99.72 1	99.39 8	96.63 10	97.60 297	98.17 23	99.85 2	99.64 56

MP-MVS-pluss		98.31 41	97.92 44	99.49 6	99.72 11	98.88 7	98.43 170	98.78 72	94.10 143	97.69 88	99.42 6	95.25 48	99.92 15	98.09 24	99.80 10	99.67 49
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SMA-MVS		98.64 11	98.33 25	99.59 2	99.51 28	99.11 3	98.95 69	98.83 58	93.77 161	99.52 3	99.52 3	96.94 5	99.89 29	98.06 25	99.84 7	99.76 20

CNVR-MVS		98.78 3	98.56 6	99.45 10	99.32 48	98.87 8	98.47 166	98.81 62	97.72 4	98.76 36	99.16 45	97.05 4	99.78 77	98.06 25	99.66 45	99.69 38

MVS_111021_HR		98.47 30	98.34 22	98.88 58	99.22 74	97.32 67	97.91 229	99.58 3	97.20 29	98.33 56	99.00 66	95.99 27	99.64 103	98.05 27	99.76 26	99.69 38

VDDNet		95.36 169	94.53 182	97.86 113	98.10 156	95.13 163	98.85 88	97.75 229	90.46 270	98.36 54	99.39 8	73.27 329	99.64 103	97.98 28	96.58 162	98.81 141

MCST-MVS		98.65 10	98.37 18	99.48 7	99.60 24	98.87 8	98.41 172	98.68 98	97.04 38	98.52 47	98.80 87	96.78 7	99.83 46	97.93 29	99.61 51	99.74 28

zzz-MVS		98.55 24	98.25 31	99.46 8	99.76 1	98.64 11	98.55 153	98.74 80	97.27 25	98.02 67	99.39 8	94.81 57	99.96 1	97.91 30	99.79 11	99.77 14

MTAPA		98.58 20	98.29 28	99.46 8	99.76 1	98.64 11	98.90 74	98.74 80	97.27 25	98.02 67	99.39 8	94.81 57	99.96 1	97.91 30	99.79 11	99.77 14

MVS_111021_LR		98.34 38	98.23 34	98.67 66	99.27 64	96.90 83	97.95 224	99.58 3	97.14 33	98.44 52	99.01 65	95.03 54	99.62 108	97.91 30	99.75 32	99.50 74

ACMMP_Plus		98.61 15	98.30 27	99.55 3	99.62 23	98.95 6	98.82 94	98.81 62	95.80 74	99.16 15	99.47 5	95.37 43	99.92 15	97.89 33	99.75 32	99.79 4

PS-MVSNAJ		97.73 57	97.77 46	97.62 132	98.68 124	95.58 145	97.34 274	98.51 133	97.29 20	98.66 40	97.88 164	94.51 63	99.90 27	97.87 34	99.17 89	97.39 198

XVS		98.70 5	98.49 12	99.34 15	99.70 15	98.35 25	99.29 14	98.88 47	97.40 14	98.46 48	99.20 38	95.90 32	99.89 29	97.85 35	99.74 35	99.78 7

X-MVStestdata		94.06 244	92.30 262	99.34 15	99.70 15	98.35 25	99.29 14	98.88 47	97.40 14	98.46 48	43.50 352	95.90 32	99.89 29	97.85 35	99.74 35	99.78 7

xiu_mvs_v2_base		97.66 62	97.70 49	97.56 140	98.61 130	95.46 151	97.44 263	98.46 143	97.15 32	98.65 41	98.15 144	94.33 69	99.80 60	97.84 37	98.66 109	97.41 196

DeepC-MVS	95.98 3	97.88 51	97.58 52	98.77 61	99.25 67	96.93 81	98.83 92	98.75 79	96.96 41	96.89 118	99.50 4	90.46 127	99.87 38	97.84 37	99.76 26	99.52 69
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 27	99.75 3	98.23 31	99.26 17	98.58 121	97.52 7	99.41 4	98.78 88	96.00 26	99.79 72	97.79 39	99.59 55	99.69 38

CP-MVS		98.57 22	98.36 19	99.19 30	99.66 19	97.86 49	99.34 11	98.87 49	95.96 70	98.60 44	99.13 47	96.05 25	99.94 3	97.77 40	99.86 1	99.77 14

SteuartSystems-ACMMP		98.90 2	98.75 2	99.36 14	99.22 74	98.43 19	99.10 51	98.87 49	97.38 17	99.35 6	99.40 7	97.78 1	99.87 38	97.77 40	99.85 2	99.78 7
Skip Steuart: Steuart Systems R&D Blog.
APD-MVS_3200maxsize		98.53 27	98.33 25	99.15 39	99.50 30	97.92 48	99.15 43	98.81 62	96.24 60	99.20 13	99.37 13	95.30 46	99.80 60	97.73 42	99.67 42	99.72 33

LFMVS		95.86 131	94.98 153	98.47 80	98.87 109	96.32 106	98.84 91	96.02 314	93.40 188	98.62 42	99.20 38	74.99 322	99.63 106	97.72 43	97.20 151	99.46 84

PHI-MVS		98.34 38	98.06 39	99.18 34	99.15 81	98.12 40	99.04 59	99.09 19	93.32 191	98.83 32	99.10 51	96.54 11	99.83 46	97.70 44	99.76 26	99.59 64

HPM-MVS		98.36 36	98.10 38	99.13 40	99.74 7	97.82 52	99.53 1	98.80 69	94.63 130	98.61 43	98.97 68	95.13 52	99.77 82	97.65 45	99.83 8	99.79 4
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HFP-MVS		98.63 14	98.40 14	99.32 18	99.72 11	98.29 28	99.23 22	98.96 31	96.10 67	98.94 24	99.17 42	96.06 23	99.92 15	97.62 46	99.78 15	99.75 23

ACMMPR		98.59 18	98.36 19	99.29 20	99.74 7	98.15 38	99.23 22	98.95 33	96.10 67	98.93 28	99.19 41	95.70 36	99.94 3	97.62 46	99.79 11	99.78 7

jason		97.32 81	97.08 75	98.06 106	97.45 196	95.59 144	97.87 236	97.91 224	94.79 123	98.55 46	98.83 84	91.12 117	99.23 147	97.58 48	99.60 52	99.34 91
jason: jason.
lupinMVS		97.44 73	97.22 70	98.12 100	98.07 157	95.76 140	97.68 251	97.76 228	94.50 134	98.79 33	98.61 103	92.34 89	99.30 141	97.58 48	99.59 55	99.31 94

HPM-MVS_fast		98.38 34	98.13 37	99.12 42	99.75 3	97.86 49	99.44 4	98.82 59	94.46 137	98.94 24	99.20 38	95.16 51	99.74 88	97.58 48	99.85 2	99.77 14

region2R		98.61 15	98.38 17	99.29 20	99.74 7	98.16 37	99.23 22	98.93 36	96.15 62	98.94 24	99.17 42	95.91 31	99.94 3	97.55 51	99.79 11	99.78 7

DeepC-MVS_fast	96.70 1	98.55 24	98.34 22	99.18 34	99.25 67	98.04 42	98.50 163	98.78 72	97.72 4	98.92 29	99.28 28	95.27 47	99.82 51	97.55 51	99.77 20	99.69 38
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 20	98.25 31	99.55 3	99.50 30	99.08 4	98.72 124	98.66 108	97.51 8	98.15 58	98.83 84	95.70 36	99.92 15	97.53 53	99.67 42	99.66 51

nrg030		96.28 120	95.72 122	97.96 110	96.90 229	98.15 38	99.39 5	98.31 163	95.47 86	94.42 196	98.35 126	92.09 99	98.69 210	97.50 54	89.05 275	97.04 212

CSCG		97.85 54	97.74 48	98.20 94	99.67 18	95.16 161	99.22 28	99.32 7	93.04 199	97.02 110	98.92 78	95.36 44	99.91 24	97.43 55	99.64 48	99.52 69

mPP-MVS		98.51 28	98.26 30	99.25 26	99.75 3	98.04 42	99.28 16	98.81 62	96.24 60	98.35 55	99.23 32	95.46 41	99.94 3	97.42 56	99.81 9	99.77 14

mvs_anonymous		96.70 103	96.53 99	97.18 161	98.19 150	93.78 234	98.31 184	98.19 183	94.01 147	94.47 187	98.27 137	92.08 100	98.46 239	97.39 57	97.91 135	99.31 94

NCCC		98.61 15	98.35 21	99.38 12	99.28 63	98.61 13	98.45 167	98.76 76	97.82 3	98.45 51	98.93 76	96.65 9	99.83 46	97.38 58	99.41 79	99.71 35

VPA-MVSNet		95.75 135	95.11 147	97.69 127	97.24 207	97.27 69	98.94 71	99.23 12	95.13 109	95.51 166	97.32 207	85.73 239	98.91 192	97.33 59	89.55 269	96.89 227

3Dnovator	94.51 5	97.46 69	96.93 80	99.07 45	97.78 174	97.64 56	99.35 10	99.06 21	97.02 39	93.75 230	99.16 45	89.25 142	99.92 15	97.22 60	99.75 32	99.64 56

#test#		98.54 26	98.27 29	99.32 18	99.72 11	98.29 28	98.98 66	98.96 31	95.65 80	98.94 24	99.17 42	96.06 23	99.92 15	97.21 61	99.78 15	99.75 23

ACMMP		98.23 43	97.95 43	99.09 44	99.74 7	97.62 58	99.03 60	99.41 6	95.98 69	97.60 94	99.36 17	94.45 67	99.93 9	97.14 62	98.85 100	99.70 37
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 59	97.46 60	98.44 82	99.27 64	95.91 134	98.63 140	99.16 17	94.48 136	97.67 89	98.88 80	92.80 85	99.91 24	97.11 63	99.12 90	99.50 74

mvs_tets		95.41 164	95.00 151	96.65 197	95.58 302	94.42 216	99.00 62	98.55 125	95.73 76	93.21 243	98.38 123	83.45 281	98.63 215	97.09 64	94.00 218	96.91 224

EPNet		97.28 82	96.87 83	98.51 76	94.98 312	96.14 111	98.90 74	97.02 284	98.28 1	95.99 163	99.11 49	91.36 114	99.89 29	96.98 65	99.19 88	99.50 74
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HyFIR lowres test		96.90 97	96.49 100	98.14 97	99.33 45	95.56 147	97.38 268	99.65 2	92.34 229	97.61 93	98.20 142	89.29 141	99.10 169	96.97 66	97.60 147	99.77 14

3Dnovator+	94.38 6	97.43 74	96.78 87	99.38 12	97.83 172	98.52 14	99.37 7	98.71 92	97.09 37	92.99 251	99.13 47	89.36 139	99.89 29	96.97 66	99.57 58	99.71 35

abl_6		98.30 42	98.03 40	99.13 40	99.56 26	97.76 54	99.13 47	98.82 59	96.14 63	99.26 9	99.37 13	93.33 79	99.93 9	96.96 68	99.67 42	99.69 38

jajsoiax		95.45 159	95.03 150	96.73 185	95.42 307	94.63 206	99.14 44	98.52 131	95.74 75	93.22 242	98.36 125	83.87 278	98.65 214	96.95 69	94.04 216	96.91 224

MVSFormer		97.57 66	97.49 58	97.84 114	98.07 157	95.76 140	99.47 2	98.40 153	94.98 116	98.79 33	98.83 84	92.34 89	98.41 254	96.91 70	99.59 55	99.34 91

test_djsdf		96.00 125	95.69 127	96.93 177	95.72 298	95.49 150	99.47 2	98.40 153	94.98 116	94.58 183	97.86 165	89.16 145	98.41 254	96.91 70	94.12 215	96.88 229

test_prior3		98.22 44	97.90 45	99.19 30	99.31 50	98.22 33	97.80 242	98.84 54	96.12 65	97.89 78	98.69 95	95.96 28	99.70 94	96.89 72	99.60 52	99.65 53

test_prior2							97.80 242		96.12 65	97.89 78	98.69 95	95.96 28		96.89 72	99.60 52

EPP-MVSNet		97.46 69	97.28 66	97.99 108	98.64 127	95.38 153	99.33 13	98.31 163	93.61 176	97.19 102	99.07 58	94.05 73	99.23 147	96.89 72	98.43 120	99.37 90

PS-MVSNAJss		96.43 112	96.26 107	96.92 179	95.84 294	95.08 165	99.16 42	98.50 138	95.87 72	93.84 228	98.34 130	94.51 63	98.61 216	96.88 75	93.45 230	97.06 210

PVSNet_BlendedMVS		96.73 102	96.60 95	97.12 165	99.25 67	95.35 156	98.26 190	99.26 8	94.28 139	97.94 74	97.46 196	92.74 86	99.81 53	96.88 75	93.32 233	96.20 290

PVSNet_Blended		97.38 78	97.12 72	98.14 97	99.25 67	95.35 156	97.28 278	99.26 8	93.13 197	97.94 74	98.21 141	92.74 86	99.81 53	96.88 75	99.40 81	99.27 101

Effi-MVS+		97.12 89	96.69 91	98.39 86	98.19 150	96.72 90	97.37 270	98.43 150	93.71 167	97.65 92	98.02 152	92.20 96	99.25 145	96.87 78	97.79 141	99.19 109

CHOSEN 1792x2688		97.12 89	96.80 84	98.08 103	99.30 55	94.56 213	98.05 214	99.71 1	93.57 177	97.09 104	98.91 79	88.17 183	99.89 29	96.87 78	99.56 64	99.81 2

PGM-MVS		98.49 29	98.23 34	99.27 25	99.72 11	98.08 41	98.99 63	99.49 5	95.43 88	99.03 18	99.32 21	95.56 38	99.94 3	96.80 80	99.77 20	99.78 7

XVG-OURS-SEG-HR		96.51 110	96.34 103	97.02 170	98.77 116	93.76 235	97.79 244	98.50 138	95.45 87	96.94 113	99.09 55	87.87 194	99.55 125	96.76 81	95.83 199	97.74 187

MP-MVS		98.33 40	98.01 41	99.28 22	99.75 3	98.18 36	99.22 28	98.79 70	96.13 64	97.92 76	99.23 32	94.54 62	99.94 3	96.74 82	99.78 15	99.73 30

test_normal		94.72 205	93.59 236	98.11 101	95.30 309	95.95 121	97.91 229	97.39 264	94.64 129	85.70 313	95.88 292	80.52 297	99.36 139	96.69 83	98.30 125	99.01 129

DI_MVS_plusplus_test		94.74 204	93.62 234	98.09 102	95.34 308	95.92 132	98.09 212	97.34 266	94.66 128	85.89 310	95.91 291	80.49 298	99.38 138	96.66 84	98.22 126	98.97 131

agg_prior1		97.95 48	97.51 57	99.28 22	99.30 55	98.38 20	97.81 241	98.72 87	93.16 196	97.57 96	98.66 100	96.14 18	99.81 53	96.63 85	99.56 64	99.66 51

train_agg		97.97 46	97.52 56	99.33 17	99.31 50	98.50 15	97.92 226	98.73 85	92.98 202	97.74 84	98.68 97	96.20 15	99.80 60	96.59 86	99.57 58	99.68 44

agg_prior3		97.87 52	97.42 62	99.23 29	99.29 58	98.23 31	97.92 226	98.72 87	92.38 228	97.59 95	98.64 102	96.09 21	99.79 72	96.59 86	99.57 58	99.68 44

MVSTER		96.06 124	95.72 122	97.08 168	98.23 146	95.93 125	98.73 122	98.27 169	94.86 122	95.07 171	98.09 148	88.21 182	98.54 223	96.59 86	93.46 228	96.79 237

UGNet		96.78 101	96.30 105	98.19 96	98.24 145	95.89 136	98.88 80	98.93 36	97.39 16	96.81 124	97.84 168	82.60 284	99.90 27	96.53 89	99.49 70	98.79 142
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 37	98.00 42	99.42 11	99.51 28	98.72 10	98.80 103	98.82 59	94.52 133	99.23 11	99.25 31	95.54 40	99.80 60	96.52 90	99.77 20	99.74 28
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
VPNet		94.99 185	94.19 198	97.40 153	97.16 215	96.57 95	98.71 125	98.97 29	95.67 78	94.84 176	98.24 140	80.36 299	98.67 213	96.46 91	87.32 300	96.96 216

sss		97.39 77	96.98 79	98.61 69	98.60 131	96.61 94	98.22 192	98.93 36	93.97 151	98.01 69	98.48 115	91.98 102	99.85 43	96.45 92	98.15 129	99.39 89

MVS_Test		97.28 82	97.00 78	98.13 99	98.33 141	95.97 118	98.74 119	98.07 214	94.27 140	98.44 52	98.07 149	92.48 88	99.26 144	96.43 93	98.19 128	99.16 114

FIs		96.51 110	96.12 111	97.67 129	97.13 217	97.54 61	99.36 8	99.22 14	95.89 71	94.03 221	98.35 126	91.98 102	98.44 244	96.40 94	92.76 240	97.01 213

test9_res														96.39 95	99.57 58	99.69 38

test_part3							98.55 153		96.40 57		99.31 22		99.93 9	96.37 96

ESAPD		98.70 5	98.39 15	99.62 1	99.63 21	99.18 1	98.55 153	98.84 54	96.40 57	99.27 7	99.31 22	97.38 2	99.93 9	96.37 96	99.78 15	99.76 20

PMMVS		96.60 105	96.33 104	97.41 151	97.90 168	93.93 230	97.35 273	98.41 151	92.84 208	97.76 82	97.45 198	91.10 119	99.20 155	96.26 98	97.91 135	99.11 119

CLD-MVS		95.62 143	95.34 136	96.46 222	97.52 190	93.75 237	97.27 279	98.46 143	95.53 84	94.42 196	98.00 155	86.21 224	98.97 182	96.25 99	94.37 205	96.66 258
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP_MVS		96.14 123	95.90 117	96.85 180	97.42 197	94.60 211	98.80 103	98.56 123	97.28 21	95.34 167	98.28 134	87.09 210	99.03 178	96.07 100	94.27 207	96.92 219

plane_prior5								98.56 123					99.03 178	96.07 100	94.27 207	96.92 219

CPTT-MVS		97.72 58	97.32 65	98.92 55	99.64 20	97.10 76	99.12 49	98.81 62	92.34 229	98.09 61	99.08 57	93.01 83	99.92 15	96.06 102	99.77 20	99.75 23

DP-MVS Recon		97.86 53	97.46 60	99.06 47	99.53 27	98.35 25	98.33 179	98.89 44	92.62 212	98.05 63	98.94 75	95.34 45	99.65 101	96.04 103	99.42 78	99.19 109

FC-MVSNet-test		96.42 113	96.05 112	97.53 141	96.95 224	97.27 69	99.36 8	99.23 12	95.83 73	93.93 223	98.37 124	92.00 101	98.32 263	96.02 104	92.72 241	97.00 214

Vis-MVSNet		97.42 75	97.11 73	98.34 88	98.66 125	96.23 109	99.22 28	99.00 26	96.63 51	98.04 65	99.21 35	88.05 188	99.35 140	96.01 105	99.21 87	99.45 86
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ab-mvs		96.42 113	95.71 125	98.55 73	98.63 128	96.75 89	97.88 235	98.74 80	93.84 157	96.54 138	98.18 143	85.34 247	99.75 86	95.93 106	96.35 173	99.15 115

WTY-MVS		97.37 79	96.92 81	98.72 63	98.86 110	96.89 85	98.31 184	98.71 92	95.26 103	97.67 89	98.56 109	92.21 95	99.78 77	95.89 107	96.85 156	99.48 79

XVG-OURS		96.55 109	96.41 101	96.99 171	98.75 117	93.76 235	97.50 262	98.52 131	95.67 78	96.83 121	99.30 27	88.95 153	99.53 126	95.88 108	96.26 182	97.69 191

agg_prior2														95.87 109	99.57 58	99.68 44

UniMVSNet_NR-MVSNet		95.71 138	95.15 146	97.40 153	96.84 232	96.97 79	98.74 119	99.24 10	95.16 108	93.88 225	97.72 180	91.68 106	98.31 265	95.81 110	87.25 302	96.92 219

DU-MVS		95.42 162	94.76 172	97.40 153	96.53 246	96.97 79	98.66 138	98.99 28	95.43 88	93.88 225	97.69 181	88.57 173	98.31 265	95.81 110	87.25 302	96.92 219

UniMVSNet (Re)		95.78 134	95.19 145	97.58 138	96.99 223	97.47 63	98.79 108	99.18 16	95.60 81	93.92 224	97.04 236	91.68 106	98.48 234	95.80 112	87.66 297	96.79 237

cascas		94.63 212	93.86 219	96.93 177	96.91 228	94.27 223	96.00 317	98.51 133	85.55 320	94.54 184	96.23 282	84.20 272	98.87 198	95.80 112	96.98 155	97.66 192

Effi-MVS+-dtu		96.29 118	96.56 96	95.51 257	97.89 169	90.22 288	98.80 103	98.10 209	96.57 52	96.45 153	96.66 266	90.81 122	98.91 192	95.72 114	97.99 133	97.40 197

mvs-test1		96.60 105	96.68 93	96.37 226	97.89 169	91.81 265	98.56 151	98.10 209	96.57 52	96.52 140	97.94 159	90.81 122	99.45 134	95.72 114	98.01 132	97.86 184

LPG-MVS_test		95.62 143	95.34 136	96.47 219	97.46 193	93.54 240	98.99 63	98.54 126	94.67 126	94.36 198	98.77 90	85.39 244	99.11 166	95.71 116	94.15 213	96.76 240

LGP-MVS_train				96.47 219	97.46 193	93.54 240		98.54 126	94.67 126	94.36 198	98.77 90	85.39 244	99.11 166	95.71 116	94.15 213	96.76 240

旧先验2							97.57 259		91.30 259	98.67 39			99.80 60	95.70 118

LCM-MVSNet-Re		95.22 177	95.32 139	94.91 280	98.18 152	87.85 317	98.75 115	95.66 326	95.11 110	88.96 298	96.85 259	90.26 132	97.65 295	95.65 119	98.44 118	99.22 106

anonymousdsp		95.42 162	94.91 161	96.94 176	95.10 311	95.90 135	99.14 44	98.41 151	93.75 162	93.16 244	97.46 196	87.50 206	98.41 254	95.63 120	94.03 217	96.50 280

CDPH-MVS		97.94 49	97.49 58	99.28 22	99.47 34	98.44 17	97.91 229	98.67 105	92.57 215	98.77 35	98.85 82	95.93 30	99.72 89	95.56 121	99.69 41	99.68 44

CostFormer		94.95 189	94.73 174	95.60 256	97.28 205	89.06 301	97.53 260	96.89 298	89.66 292	96.82 123	96.72 264	86.05 234	98.95 189	95.53 122	96.13 188	98.79 142

ACMM	93.85 9	95.69 140	95.38 135	96.61 203	97.61 182	93.84 233	98.91 73	98.44 147	95.25 104	94.28 206	98.47 116	86.04 236	99.12 162	95.50 123	93.95 220	96.87 230
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	93.49 10	95.34 171	94.98 153	96.43 223	97.67 178	93.48 242	98.73 122	98.44 147	94.94 121	92.53 261	98.53 110	84.50 263	99.14 160	95.48 124	94.00 218	96.66 258
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
testing_2		90.61 294	88.50 301	96.95 175	90.08 334	95.57 146	97.69 250	98.06 216	93.02 200	76.55 336	92.48 332	61.18 343	98.44 244	95.45 125	91.98 247	96.84 233

Test4		92.21 270	90.34 286	97.82 117	92.83 326	95.87 138	97.94 225	98.05 219	94.50 134	82.12 329	94.48 308	59.54 344	98.54 223	95.39 126	98.22 126	99.06 125

TAMVS		97.02 92	96.79 86	97.70 126	98.06 159	95.31 158	98.52 158	98.31 163	93.95 152	97.05 109	98.61 103	93.49 78	98.52 230	95.33 127	97.81 140	99.29 99

BP-MVS														95.30 128

HQP-MVS		95.72 136	95.40 131	96.69 189	97.20 211	94.25 224	98.05 214	98.46 143	96.43 54	94.45 188	97.73 177	86.75 216	98.96 185	95.30 128	94.18 211	96.86 232

WR-MVS		95.15 180	94.46 185	97.22 158	96.67 242	96.45 100	98.21 193	98.81 62	94.15 141	93.16 244	97.69 181	87.51 204	98.30 267	95.29 130	88.62 286	96.90 226

PatchFormer-LS_test		95.47 157	95.27 142	96.08 241	97.59 184	90.66 282	98.10 211	97.34 266	93.98 150	96.08 159	96.15 286	87.65 202	99.12 162	95.27 131	95.24 203	98.44 161

tpmrst		95.63 142	95.69 127	95.44 263	97.54 188	88.54 310	96.97 288	97.56 236	93.50 179	97.52 98	96.93 251	89.49 136	99.16 157	95.25 132	96.42 168	98.64 152

CDS-MVSNet		96.99 93	96.69 91	97.90 112	98.05 160	95.98 114	98.20 194	98.33 162	93.67 174	96.95 111	98.49 114	93.54 77	98.42 247	95.24 133	97.74 144	99.31 94
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
OPM-MVS		95.69 140	95.33 138	96.76 184	96.16 281	94.63 206	98.43 170	98.39 155	96.64 50	95.02 173	98.78 88	85.15 249	99.05 173	95.21 134	94.20 210	96.60 267

OMC-MVS		97.55 68	97.34 64	98.20 94	99.33 45	95.92 132	98.28 188	98.59 116	95.52 85	97.97 72	99.10 51	93.28 81	99.49 129	95.09 135	98.88 97	99.19 109

CANet_DTU		96.96 94	96.55 97	98.21 93	98.17 154	96.07 113	97.98 221	98.21 179	97.24 27	97.13 103	98.93 76	86.88 215	99.91 24	95.00 136	99.37 83	98.66 150

UA-Net		97.96 47	97.62 50	98.98 51	98.86 110	97.47 63	98.89 78	99.08 20	96.67 49	98.72 38	99.54 1	93.15 82	99.81 53	94.87 137	98.83 101	99.65 53

114514_t		96.93 95	96.27 106	98.92 55	99.50 30	97.63 57	98.85 88	98.90 42	84.80 324	97.77 81	99.11 49	92.84 84	99.66 100	94.85 138	99.77 20	99.47 80

XXY-MVS		95.20 179	94.45 187	97.46 148	96.75 237	96.56 96	98.86 87	98.65 112	93.30 193	93.27 241	98.27 137	84.85 254	98.87 198	94.82 139	91.26 257	96.96 216

MG-MVS		97.81 55	97.60 51	98.44 82	99.12 83	95.97 118	97.75 246	98.78 72	96.89 42	98.46 48	99.22 34	93.90 76	99.68 98	94.81 140	99.52 69	99.67 49

EI-MVSNet		95.96 126	95.83 119	96.36 227	97.93 166	93.70 239	98.12 207	98.27 169	93.70 169	95.07 171	99.02 61	92.23 94	98.54 223	94.68 141	93.46 228	96.84 233

IterMVS-LS		95.46 158	95.21 144	96.22 235	98.12 155	93.72 238	98.32 183	98.13 197	93.71 167	94.26 207	97.31 208	92.24 93	98.10 276	94.63 142	90.12 261	96.84 233
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.25 122	95.73 121	97.79 118	97.13 217	95.55 149	98.19 198	98.59 116	93.47 180	92.03 273	97.82 172	91.33 115	99.49 129	94.62 143	98.44 118	98.32 171

IS-MVSNet		97.22 84	96.88 82	98.25 92	98.85 112	96.36 104	99.19 34	97.97 221	95.39 90	97.23 101	98.99 67	91.11 118	98.93 190	94.60 144	98.59 111	99.47 80

NR-MVSNet		94.98 187	94.16 199	97.44 149	96.53 246	97.22 73	98.74 119	98.95 33	94.96 118	89.25 296	97.69 181	89.32 140	98.18 273	94.59 145	87.40 299	96.92 219

IB-MVS	91.98 17	93.27 258	91.97 265	97.19 160	97.47 192	93.41 245	97.09 286	95.99 315	93.32 191	92.47 264	95.73 295	78.06 308	99.53 126	94.59 145	82.98 319	98.62 153
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		94.82 197	94.36 190	96.20 236	97.35 202	90.79 279	98.34 178	96.57 309	92.91 205	95.33 169	96.44 276	82.00 286	99.12 162	94.52 147	95.78 200	98.70 146

HY-MVS	93.96 8	96.82 100	96.23 109	98.57 71	98.46 136	97.00 78	98.14 204	98.21 179	93.95 152	96.72 127	97.99 156	91.58 108	99.76 84	94.51 148	96.54 164	98.95 135

Baseline_NR-MVSNet		94.35 226	93.81 221	95.96 243	96.20 276	94.05 228	98.61 143	96.67 306	91.44 250	93.85 227	97.60 189	88.57 173	98.14 274	94.39 149	86.93 305	95.68 303

AdaColmap		97.15 88	96.70 90	98.48 79	99.16 79	96.69 91	98.01 218	98.89 44	94.44 138	96.83 121	98.68 97	90.69 125	99.76 84	94.36 150	99.29 86	98.98 130

1112_ss		96.63 104	96.00 115	98.50 77	98.56 132	96.37 103	98.18 202	98.10 209	92.92 204	94.84 176	98.43 118	92.14 97	99.58 116	94.35 151	96.51 165	99.56 68

CP-MVSNet		94.94 191	94.30 192	96.83 181	96.72 239	95.56 147	99.11 50	98.95 33	93.89 154	92.42 266	97.90 162	87.19 209	98.12 275	94.32 152	88.21 289	96.82 236

CNLPA		97.45 72	97.03 77	98.73 62	99.05 84	97.44 65	98.07 213	98.53 129	95.32 101	96.80 125	98.53 110	93.32 80	99.72 89	94.31 153	99.31 85	99.02 126

testdata				98.26 91	99.20 77	95.36 154		98.68 98	91.89 239	98.60 44	99.10 51	94.44 68	99.82 51	94.27 154	99.44 77	99.58 66

PVSNet	91.96 18	96.35 115	96.15 110	96.96 174	99.17 78	92.05 262	96.08 313	98.68 98	93.69 170	97.75 83	97.80 174	88.86 156	99.69 97	94.26 155	99.01 92	99.15 115

Test_1112_low_res		96.34 116	95.66 129	98.36 87	98.56 132	95.94 122	97.71 248	98.07 214	92.10 235	94.79 180	97.29 209	91.75 105	99.56 119	94.17 156	96.50 166	99.58 66

TranMVSNet+NR-MVSNet		95.14 181	94.48 183	97.11 166	96.45 251	96.36 104	99.03 60	99.03 24	95.04 114	93.58 232	97.93 160	88.27 181	98.03 281	94.13 157	86.90 307	96.95 218

API-MVS		97.41 76	97.25 67	97.91 111	98.70 121	96.80 86	98.82 94	98.69 95	94.53 132	98.11 60	98.28 134	94.50 66	99.57 117	94.12 158	99.49 70	97.37 200

PLC	95.07 4	97.20 85	96.78 87	98.44 82	99.29 58	96.31 108	98.14 204	98.76 76	92.41 226	96.39 154	98.31 133	94.92 56	99.78 77	94.06 159	98.77 104	99.23 105
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
XVG-ACMP-BASELINE		94.54 218	94.14 201	95.75 253	96.55 245	91.65 270	98.11 209	98.44 147	94.96 118	94.22 210	97.90 162	79.18 305	99.11 166	94.05 160	93.85 221	96.48 282

F-COLMAP		97.09 91	96.80 84	97.97 109	99.45 36	94.95 173	98.55 153	98.62 114	93.02 200	96.17 158	98.58 108	94.01 74	99.81 53	93.95 161	98.90 96	99.14 117

MDTV_nov1_ep13_2view						84.26 325	96.89 296		90.97 267	97.90 77		89.89 135		93.91 162		99.18 113

diffmvs		96.32 117	95.74 120	98.07 105	98.26 144	96.14 111	98.53 157	98.23 177	90.10 278	96.88 119	97.73 177	90.16 133	99.15 158	93.90 163	97.85 139	98.91 137

原ACMM1				98.65 67	99.32 48	96.62 92		98.67 105	93.27 194	97.81 80	98.97 68	95.18 50	99.83 46	93.84 164	99.46 75	99.50 74

RPSCF		94.87 193	95.40 131	93.26 307	98.89 107	82.06 332	98.33 179	98.06 216	90.30 274	96.56 134	99.26 30	87.09 210	99.49 129	93.82 165	96.32 175	98.24 172

PAPM_NR		97.46 69	97.11 73	98.50 77	99.50 30	96.41 102	98.63 140	98.60 115	95.18 107	97.06 108	98.06 150	94.26 71	99.57 117	93.80 166	98.87 99	99.52 69

ACMH	92.88 16	94.55 217	93.95 214	96.34 230	97.63 180	93.26 247	98.81 100	98.49 142	93.43 181	89.74 291	98.53 110	81.91 287	99.08 171	93.69 167	93.30 234	96.70 249
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MAR-MVS		96.91 96	96.40 102	98.45 81	98.69 123	96.90 83	98.66 138	98.68 98	92.40 227	97.07 107	97.96 157	91.54 112	99.75 86	93.68 168	98.92 95	98.69 147
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 98	96.55 97	97.83 115	98.73 118	95.46 151	99.20 32	98.30 166	94.96 118	96.60 133	98.87 81	90.05 134	98.59 219	93.67 169	98.60 110	99.46 84

LS3D		97.16 87	96.66 94	98.68 65	98.53 135	97.19 74	98.93 72	98.90 42	92.83 209	95.99 163	99.37 13	92.12 98	99.87 38	93.67 169	99.57 58	98.97 131

PS-CasMVS		94.67 210	93.99 212	96.71 186	96.68 241	95.26 159	99.13 47	99.03 24	93.68 172	92.33 267	97.95 158	85.35 246	98.10 276	93.59 171	88.16 291	96.79 237

CVMVSNet		95.43 160	96.04 113	93.57 303	97.93 166	83.62 326	98.12 207	98.59 116	95.68 77	96.56 134	99.02 61	87.51 204	97.51 300	93.56 172	97.44 148	99.60 62

OurMVSNet-221017-0		94.21 232	94.00 210	94.85 283	95.60 301	89.22 299	98.89 78	97.43 259	95.29 102	92.18 271	98.52 113	82.86 283	98.59 219	93.46 173	91.76 251	96.74 242

OpenMVS	93.04 13	95.83 132	95.00 151	98.32 89	97.18 214	97.32 67	99.21 31	98.97 29	89.96 281	91.14 279	99.05 60	86.64 218	99.92 15	93.38 174	99.47 72	97.73 188

无先验							97.58 258	98.72 87	91.38 253				99.87 38	93.36 175		99.60 62

1121		97.37 79	96.77 89	99.16 37	99.34 42	97.99 47	98.19 198	98.68 98	90.14 277	98.01 69	98.97 68	94.80 59	99.87 38	93.36 175	99.46 75	99.61 59

gm-plane-assit					95.88 292	87.47 318			89.74 290		96.94 247		99.19 156	93.32 177

WR-MVS_H		95.05 183	94.46 185	96.81 182	96.86 231	95.82 139	99.24 20	99.24 10	93.87 156	92.53 261	96.84 260	90.37 128	98.24 271	93.24 178	87.93 292	96.38 285

tpm		94.13 240	93.80 222	95.12 276	96.50 248	87.91 316	97.44 263	95.89 319	92.62 212	96.37 155	96.30 279	84.13 273	98.30 267	93.24 178	91.66 253	99.14 117

Fast-Effi-MVS+-dtu		95.87 130	95.85 118	95.91 245	97.74 176	91.74 269	98.69 129	98.15 194	95.56 83	94.92 174	97.68 184	88.98 151	98.79 207	93.19 180	97.78 142	97.20 208

pmmvs5		93.65 253	92.97 252	95.68 254	95.49 305	92.37 258	98.20 194	97.28 272	89.66 292	92.58 259	97.26 210	82.14 285	98.09 278	93.18 181	90.95 258	96.58 269

TESTMET0.1,1		94.18 236	93.69 231	95.63 255	96.92 226	89.12 300	96.91 292	94.78 335	93.17 195	94.88 175	96.45 275	78.52 306	98.92 191	93.09 182	98.50 115	98.85 138

test-LLR		95.10 182	94.87 163	95.80 250	96.77 234	89.70 292	96.91 292	95.21 330	95.11 110	94.83 178	95.72 297	87.71 198	98.97 182	93.06 183	98.50 115	98.72 144

test-mter		94.08 242	93.51 242	95.80 250	96.77 234	89.70 292	96.91 292	95.21 330	92.89 206	94.83 178	95.72 297	77.69 310	98.97 182	93.06 183	98.50 115	98.72 144

BH-untuned		95.95 127	95.72 122	96.65 197	98.55 134	92.26 259	98.23 191	97.79 227	93.73 165	94.62 182	98.01 154	88.97 152	99.00 181	93.04 185	98.51 114	98.68 148

EPMVS		94.99 185	94.48 183	96.52 215	97.22 209	91.75 268	97.23 280	91.66 347	94.11 142	97.28 100	96.81 261	85.70 240	98.84 201	93.04 185	97.28 150	98.97 131

pmmvs4		94.69 206	93.99 212	96.81 182	95.74 296	95.94 122	97.40 266	97.67 232	90.42 272	93.37 239	97.59 190	89.08 147	98.20 272	92.97 187	91.67 252	96.30 289

v6		94.83 194	94.21 196	96.69 189	96.36 258	94.85 180	98.87 81	98.11 204	92.46 216	94.44 194	97.05 235	88.76 167	98.57 221	92.95 188	88.92 278	96.65 260

v1neww		94.83 194	94.22 194	96.68 192	96.39 254	94.85 180	98.87 81	98.11 204	92.45 221	94.45 188	97.06 231	88.82 161	98.54 223	92.93 189	88.91 279	96.65 260

v7new		94.83 194	94.22 194	96.68 192	96.39 254	94.85 180	98.87 81	98.11 204	92.45 221	94.45 188	97.06 231	88.82 161	98.54 223	92.93 189	88.91 279	96.65 260

v2v482		94.69 206	94.03 208	96.65 197	96.17 278	94.79 199	98.67 136	98.08 213	92.72 210	94.00 222	97.16 215	87.69 201	98.45 241	92.91 191	88.87 281	96.72 245

Fast-Effi-MVS+		96.28 120	95.70 126	98.03 107	98.29 143	95.97 118	98.58 146	98.25 174	91.74 243	95.29 170	97.23 212	91.03 121	99.15 158	92.90 192	97.96 134	98.97 131

V42		94.78 199	94.14 201	96.70 188	96.33 265	95.22 160	98.97 67	98.09 212	92.32 231	94.31 202	97.06 231	88.39 179	98.55 222	92.90 192	88.87 281	96.34 287

DP-MVS		96.59 107	95.93 116	98.57 71	99.34 42	96.19 110	98.70 128	98.39 155	89.45 296	94.52 185	99.35 19	91.85 104	99.85 43	92.89 194	98.88 97	99.68 44

TDRefinement		91.06 289	89.68 292	95.21 273	85.35 342	91.49 271	98.51 162	97.07 280	91.47 248	88.83 299	97.84 168	77.31 314	99.09 170	92.79 195	77.98 336	95.04 312

ACMH+	92.99 14	94.30 228	93.77 225	95.88 247	97.81 173	92.04 263	98.71 125	98.37 158	93.99 149	90.60 286	98.47 116	80.86 294	99.05 173	92.75 196	92.40 243	96.55 274

divwei89l23v2f112		94.76 200	94.12 204	96.67 195	96.28 271	94.85 180	98.69 129	98.12 199	92.44 223	94.29 205	96.94 247	88.85 158	98.48 234	92.67 197	88.79 285	96.67 255

v1		94.75 202	94.11 205	96.69 189	96.27 273	94.87 178	98.69 129	98.12 199	92.43 224	94.32 201	96.94 247	88.71 170	98.54 223	92.66 198	88.84 284	96.67 255

v1141		94.75 202	94.11 205	96.67 195	96.27 273	94.86 179	98.69 129	98.12 199	92.43 224	94.31 202	96.94 247	88.78 166	98.48 234	92.63 199	88.85 283	96.67 255

test_post1							96.68 303				30.43 356	87.85 195	98.69 210	92.59 200

v148		94.29 229	93.76 227	95.91 245	96.10 282	92.93 253	98.58 146	97.97 221	92.59 214	93.47 238	96.95 245	88.53 176	98.32 263	92.56 201	87.06 304	96.49 281

PEN-MVS		94.42 223	93.73 229	96.49 217	96.28 271	94.84 189	99.17 35	99.00 26	93.51 178	92.23 269	97.83 171	86.10 233	97.90 288	92.55 202	86.92 306	96.74 242

Patchmatch-RL test		91.49 284	90.85 276	93.41 304	91.37 330	84.40 324	92.81 339	95.93 318	91.87 241	87.25 304	94.87 305	88.99 148	96.53 324	92.54 203	82.00 321	99.30 97

IterMVS		94.09 241	93.85 220	94.80 286	97.99 163	90.35 287	97.18 283	98.12 199	93.68 172	92.46 265	97.34 205	84.05 274	97.41 302	92.51 204	91.33 254	96.62 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
semantic-postprocess				94.85 283	97.98 165	90.56 285		98.11 204	93.75 162	92.58 259	97.48 195	83.91 276	97.41 302	92.48 205	91.30 255	96.58 269

tpm2		94.19 234	93.76 227	95.46 261	97.23 208	89.04 302	97.31 277	96.85 301	87.08 311	96.21 157	96.79 262	83.75 280	98.74 209	92.43 206	96.23 184	98.59 154

PVSNet_0	88.72 19	91.28 286	90.03 289	95.00 279	97.99 163	87.29 320	94.84 330	98.50 138	92.06 236	89.86 290	95.19 301	79.81 301	99.39 137	92.27 207	69.79 343	98.33 170

gg-mvs-nofinetune		92.21 270	90.58 284	97.13 164	96.75 237	95.09 164	95.85 319	89.40 350	85.43 321	94.50 186	81.98 343	80.80 295	98.40 260	92.16 208	98.33 123	97.88 183

pm-mvs1		93.94 247	93.06 250	96.59 205	96.49 249	95.16 161	98.95 69	98.03 220	92.32 231	91.08 280	97.84 168	84.54 262	98.41 254	92.16 208	86.13 313	96.19 291

K. test v3		92.55 266	91.91 267	94.48 293	95.64 300	89.24 298	99.07 56	94.88 334	94.04 146	86.78 306	97.59 190	77.64 313	97.64 296	92.08 210	89.43 271	96.57 271

GBi-Net		94.49 219	93.80 222	96.56 210	98.21 147	95.00 167	98.82 94	98.18 186	92.46 216	94.09 217	97.07 228	81.16 289	97.95 285	92.08 210	92.14 244	96.72 245

test1		94.49 219	93.80 222	96.56 210	98.21 147	95.00 167	98.82 94	98.18 186	92.46 216	94.09 217	97.07 228	81.16 289	97.95 285	92.08 210	92.14 244	96.72 245

FMVSNet3		94.97 188	94.26 193	97.11 166	98.18 152	96.62 92	98.56 151	98.26 173	93.67 174	94.09 217	97.10 224	84.25 269	98.01 282	92.08 210	92.14 244	96.70 249

PatchmatchNet		95.71 138	95.52 130	96.29 233	97.58 185	90.72 281	96.84 299	97.52 242	94.06 145	97.08 105	96.96 244	89.24 143	98.90 195	92.03 214	98.37 121	99.26 102
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
QAPM		96.29 118	95.40 131	98.96 53	97.85 171	97.60 59	99.23 22	98.93 36	89.76 288	93.11 248	99.02 61	89.11 146	99.93 9	91.99 215	99.62 50	99.34 91

新几何1				99.16 37	99.34 42	98.01 44		98.69 95	90.06 279	98.13 59	98.95 74	94.60 61	99.89 29	91.97 216	99.47 72	99.59 64

v7		94.69 206	94.04 207	96.62 202	96.41 253	94.79 199	98.78 110	98.13 197	91.89 239	94.30 204	97.16 215	88.13 186	98.45 241	91.96 217	89.65 266	96.61 265

MDTV_nov1_ep13			95.40 131		97.48 191	88.34 312	96.85 298	97.29 271	93.74 164	97.48 99	97.26 210	89.18 144	99.05 173	91.92 218	97.43 149

EU-MVSNet		93.66 251	94.14 201	92.25 312	95.96 288	83.38 327	98.52 158	98.12 199	94.69 124	92.61 258	98.13 146	87.36 208	96.39 326	91.82 219	90.00 263	96.98 215

GA-MVS		94.81 198	94.03 208	97.14 163	97.15 216	93.86 232	96.76 301	97.58 235	94.00 148	94.76 181	97.04 236	80.91 292	98.48 234	91.79 220	96.25 183	99.09 120

tfpn1000		95.72 136	95.11 147	97.58 138	99.00 89	95.73 142	99.24 20	95.49 328	94.08 144	96.87 120	97.45 198	85.81 238	99.30 141	91.78 221	96.22 186	97.71 190

PatchMatch-RL		96.59 107	96.03 114	98.27 90	99.31 50	96.51 98	97.91 229	99.06 21	93.72 166	96.92 116	98.06 150	88.50 178	99.65 101	91.77 222	99.00 93	98.66 150

v1144		94.59 215	93.92 215	96.60 204	96.21 275	94.78 201	98.59 144	98.14 196	91.86 242	94.21 211	97.02 238	87.97 189	98.41 254	91.72 223	89.57 267	96.61 265

v8		94.47 221	93.77 225	96.57 209	96.36 258	94.83 191	99.05 57	98.19 183	91.92 238	93.16 244	96.97 243	88.82 161	98.48 234	91.69 224	87.79 295	96.39 284

testdata2													99.89 29	91.65 225

BH-w/o		95.38 166	95.08 149	96.26 234	98.34 140	91.79 266	97.70 249	97.43 259	92.87 207	94.24 209	97.22 213	88.66 171	98.84 201	91.55 226	97.70 145	98.16 174

tfpn_ndepth		95.53 151	94.90 162	97.39 156	98.96 96	95.88 137	99.05 57	95.27 329	93.80 160	96.95 111	96.93 251	85.53 242	99.40 135	91.54 227	96.10 189	96.89 227

v52		94.18 236	93.52 240	96.13 239	95.95 289	94.29 222	99.23 22	98.21 179	91.42 251	92.84 253	96.89 254	87.85 195	98.53 229	91.51 228	87.81 293	95.57 306

V4		94.18 236	93.52 240	96.13 239	95.89 291	94.31 221	99.23 22	98.22 178	91.42 251	92.82 254	96.89 254	87.93 191	98.52 230	91.51 228	87.81 293	95.58 305

LF4IMVS		93.14 262	92.79 255	94.20 298	95.88 292	88.67 307	97.66 253	97.07 280	93.81 159	91.71 275	97.65 185	77.96 309	98.81 205	91.47 230	91.92 249	95.12 309

JIA-IIPM		93.35 255	92.49 259	95.92 244	96.48 250	90.65 283	95.01 326	96.96 290	85.93 318	96.08 159	87.33 339	87.70 200	98.78 208	91.35 231	95.58 201	98.34 169

Patchmatch-test1		95.32 173	94.97 155	96.35 228	97.67 178	91.29 274	97.33 275	97.60 234	94.68 125	96.92 116	96.95 245	83.97 275	98.50 233	91.33 232	98.32 124	99.25 103

FMVSNet2		94.47 221	93.61 235	97.04 169	98.21 147	96.43 101	98.79 108	98.27 169	92.46 216	93.50 237	97.09 226	81.16 289	98.00 283	91.09 233	91.93 248	96.70 249

v144192		94.39 225	93.70 230	96.48 218	96.06 284	94.35 220	98.58 146	98.16 193	91.45 249	94.33 200	97.02 238	87.50 206	98.45 241	91.08 234	89.11 274	96.63 263

tpmvs		94.60 213	94.36 190	95.33 272	97.46 193	88.60 308	96.88 297	97.68 231	91.29 260	93.80 229	96.42 277	88.58 172	99.24 146	91.06 235	96.04 196	98.17 173

LTVRE_ROB	92.95 15	94.60 213	93.90 217	96.68 192	97.41 200	94.42 216	98.52 158	98.59 116	91.69 244	91.21 278	98.35 126	84.87 253	99.04 177	91.06 235	93.44 231	96.60 267
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 99	96.24 108	98.65 67	98.72 120	96.92 82	97.36 272	98.57 122	93.33 190	96.67 128	97.57 192	94.30 70	99.56 119	91.05 237	98.59 111	99.47 80

SixPastTwentyTwo		93.34 256	92.86 253	94.75 287	95.67 299	89.41 297	98.75 115	96.67 306	93.89 154	90.15 289	98.25 139	80.87 293	98.27 270	90.90 238	90.64 259	96.57 271

COLMAP_ROB	93.27 12	95.33 172	94.87 163	96.71 186	99.29 58	93.24 248	98.58 146	98.11 204	89.92 284	93.57 233	99.10 51	86.37 222	99.79 72	90.78 239	98.10 131	97.09 209
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
pmmvs6		91.77 282	90.63 283	95.17 275	94.69 318	91.24 275	98.67 136	97.92 223	86.14 315	89.62 292	97.56 193	75.79 319	98.34 261	90.75 240	84.56 318	95.94 297

BH-RMVSNet		95.92 129	95.32 139	97.69 127	98.32 142	94.64 205	98.19 198	97.45 257	94.56 131	96.03 161	98.61 103	85.02 250	99.12 162	90.68 241	99.06 91	99.30 97

v748		93.75 250	93.06 250	95.82 249	95.73 297	92.64 256	99.25 19	98.24 176	91.60 246	92.22 270	96.52 273	87.60 203	98.46 239	90.64 242	85.72 314	96.36 286

tpmp4_e23		93.91 248	93.42 247	95.38 269	97.62 181	88.59 309	97.52 261	97.34 266	87.94 307	94.17 214	96.79 262	82.91 282	99.05 173	90.62 243	95.91 197	98.50 157

DTE-MVSNet		93.98 246	93.26 249	96.14 238	96.06 284	94.39 218	99.20 32	98.86 52	93.06 198	91.78 274	97.81 173	85.87 237	97.58 298	90.53 244	86.17 311	96.46 283

conf0.01		95.56 149	94.84 165	97.72 121	98.90 99	95.93 125	99.17 35	95.70 320	93.42 182	96.50 145	97.16 215	86.12 226	99.22 149	90.51 245	96.06 190	98.02 177

conf0.002		95.56 149	94.84 165	97.72 121	98.90 99	95.93 125	99.17 35	95.70 320	93.42 182	96.50 145	97.16 215	86.12 226	99.22 149	90.51 245	96.06 190	98.02 177

thresconf0.02		95.50 152	94.84 165	97.51 142	98.90 99	95.93 125	99.17 35	95.70 320	93.42 182	96.50 145	97.16 215	86.12 226	99.22 149	90.51 245	96.06 190	97.37 200

tfpn_n400		95.50 152	94.84 165	97.51 142	98.90 99	95.93 125	99.17 35	95.70 320	93.42 182	96.50 145	97.16 215	86.12 226	99.22 149	90.51 245	96.06 190	97.37 200

tfpnconf		95.50 152	94.84 165	97.51 142	98.90 99	95.93 125	99.17 35	95.70 320	93.42 182	96.50 145	97.16 215	86.12 226	99.22 149	90.51 245	96.06 190	97.37 200

tfpnview11		95.50 152	94.84 165	97.51 142	98.90 99	95.93 125	99.17 35	95.70 320	93.42 182	96.50 145	97.16 215	86.12 226	99.22 149	90.51 245	96.06 190	97.37 200

v10		94.29 229	93.55 238	96.51 216	96.39 254	94.80 196	98.99 63	98.19 183	91.35 256	93.02 250	96.99 241	88.09 187	98.41 254	90.50 251	88.41 288	96.33 288

ambc				89.49 318	86.66 341	75.78 339	92.66 340	96.72 303		86.55 308	92.50 331	46.01 348	97.90 288	90.32 252	82.09 320	94.80 315

lessismore_v0				94.45 296	94.93 314	88.44 311		91.03 348		86.77 307	97.64 187	76.23 317	98.42 247	90.31 253	85.64 315	96.51 279

v1192		94.32 227	93.58 237	96.53 214	96.10 282	94.45 215	98.50 163	98.17 191	91.54 247	94.19 212	97.06 231	86.95 214	98.43 246	90.14 254	89.57 267	96.70 249

MVS		94.67 210	93.54 239	98.08 103	96.88 230	96.56 96	98.19 198	98.50 138	78.05 338	92.69 256	98.02 152	91.07 120	99.63 106	90.09 255	98.36 122	98.04 176

ADS-MVSNet2		94.58 216	94.40 189	95.11 277	98.00 161	88.74 305	96.04 314	97.30 270	90.15 275	96.47 151	96.64 268	87.89 192	97.56 299	90.08 256	97.06 152	99.02 126

ADS-MVSNet		95.00 184	94.45 187	96.63 200	98.00 161	91.91 264	96.04 314	97.74 230	90.15 275	96.47 151	96.64 268	87.89 192	98.96 185	90.08 256	97.06 152	99.02 126

MSDG		95.93 128	95.30 141	97.83 115	98.90 99	95.36 154	96.83 300	98.37 158	91.32 258	94.43 195	98.73 94	90.27 131	99.60 109	90.05 258	98.82 102	98.52 156

v1921920		94.20 233	93.47 244	96.40 225	95.98 287	94.08 227	98.52 158	98.15 194	91.33 257	94.25 208	97.20 214	86.41 221	98.42 247	90.04 259	89.39 272	96.69 254

dp		94.15 239	93.90 217	94.90 281	97.31 204	86.82 322	96.97 288	97.19 277	91.22 264	96.02 162	96.61 270	85.51 243	99.02 180	90.00 260	94.30 206	98.85 138

CMPMVS	66.06 21	89.70 298	89.67 293	89.78 317	93.19 324	76.56 337	97.00 287	98.35 160	80.97 334	81.57 331	97.75 176	74.75 324	98.61 216	89.85 261	93.63 225	94.17 328
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testpf		88.74 303	89.09 296	87.69 321	95.78 295	83.16 329	84.05 349	94.13 343	85.22 322	90.30 287	94.39 310	74.92 323	95.80 328	89.77 262	93.28 236	84.10 345

TR-MVS		94.94 191	94.20 197	97.17 162	97.75 175	94.14 226	97.59 257	97.02 284	92.28 233	95.75 165	97.64 187	83.88 277	98.96 185	89.77 262	96.15 187	98.40 162

MS-PatchMatch		93.84 249	93.63 233	94.46 295	96.18 277	89.45 295	97.76 245	98.27 169	92.23 234	92.13 272	97.49 194	79.50 302	98.69 210	89.75 264	99.38 82	95.25 308

ITE_SJBPF				95.44 263	97.42 197	91.32 273		97.50 248	95.09 113	93.59 231	98.35 126	81.70 288	98.88 197	89.71 265	93.39 232	96.12 292

MVP-Stereo		94.28 231	93.92 215	95.35 271	94.95 313	92.60 257	97.97 222	97.65 233	91.61 245	90.68 285	97.09 226	86.32 223	98.42 247	89.70 266	99.34 84	95.02 313
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
AllTest		95.24 176	94.65 177	96.99 171	99.25 67	93.21 249	98.59 144	98.18 186	91.36 254	93.52 235	98.77 90	84.67 255	99.72 89	89.70 266	97.87 137	98.02 177

TestCases				96.99 171	99.25 67	93.21 249		98.18 186	91.36 254	93.52 235	98.77 90	84.67 255	99.72 89	89.70 266	97.87 137	98.02 177

GG-mvs-BLEND				96.59 205	96.34 261	94.98 170	96.51 311	88.58 351		93.10 249	94.34 311	80.34 300	98.05 280	89.53 269	96.99 154	96.74 242

USDC		93.33 257	92.71 256	95.21 273	96.83 233	90.83 278	96.91 292	97.50 248	93.84 157	90.72 284	98.14 145	77.69 310	98.82 204	89.51 270	93.21 237	95.97 296

v7n		94.19 234	93.43 245	96.47 219	95.90 290	94.38 219	99.26 17	98.34 161	91.99 237	92.76 255	97.13 223	88.31 180	98.52 230	89.48 271	87.70 296	96.52 277

PM-MVS		87.77 306	86.55 308	91.40 315	91.03 332	83.36 328	96.92 290	95.18 332	91.28 261	86.48 309	93.42 314	53.27 345	96.74 318	89.43 272	81.97 322	94.11 329

FMVSNet1		93.19 261	92.07 264	96.56 210	97.54 188	95.00 167	98.82 94	98.18 186	90.38 273	92.27 268	97.07 228	73.68 328	97.95 285	89.36 273	91.30 255	96.72 245

tpm cat1		93.36 254	92.80 254	95.07 278	97.58 185	87.97 315	96.76 301	97.86 225	82.17 332	93.53 234	96.04 289	86.13 225	99.13 161	89.24 274	95.87 198	98.10 175

UnsupCasMVSNet_eth		90.99 290	89.92 291	94.19 299	94.08 321	89.83 290	97.13 285	98.67 105	93.69 170	85.83 312	96.19 285	75.15 321	96.74 318	89.14 275	79.41 330	96.00 295

v1240		94.06 244	93.29 248	96.34 230	96.03 286	93.90 231	98.44 168	98.17 191	91.18 265	94.13 216	97.01 240	86.05 234	98.42 247	89.13 276	89.50 270	96.70 249

view600		95.60 145	94.93 157	97.62 132	99.05 84	94.85 180	99.09 52	97.01 286	95.36 95	96.52 140	97.37 201	84.55 258	99.59 110	89.07 277	96.39 169	98.40 162

view800		95.60 145	94.93 157	97.62 132	99.05 84	94.85 180	99.09 52	97.01 286	95.36 95	96.52 140	97.37 201	84.55 258	99.59 110	89.07 277	96.39 169	98.40 162

conf0.05thres1000		95.60 145	94.93 157	97.62 132	99.05 84	94.85 180	99.09 52	97.01 286	95.36 95	96.52 140	97.37 201	84.55 258	99.59 110	89.07 277	96.39 169	98.40 162

tfpn		95.60 145	94.93 157	97.62 132	99.05 84	94.85 180	99.09 52	97.01 286	95.36 95	96.52 140	97.37 201	84.55 258	99.59 110	89.07 277	96.39 169	98.40 162

tmp_tt		68.90 322	66.97 322	74.68 337	50.78 357	59.95 353	87.13 345	83.47 356	38.80 352	62.21 346	96.23 282	64.70 341	76.91 355	88.91 281	30.49 352	87.19 342

v18		92.10 272	90.97 272	95.50 258	96.34 261	94.85 180	98.82 94	97.52 242	89.99 280	85.31 317	93.26 316	88.90 155	96.92 309	88.82 282	79.77 328	94.73 316

v17		92.08 273	90.94 273	95.48 260	96.34 261	94.83 191	98.81 100	97.52 242	89.95 282	85.32 315	93.24 317	88.91 154	96.91 310	88.76 283	79.63 329	94.71 318

pmmvs-eth3d		90.36 295	89.05 298	94.32 297	91.10 331	92.12 260	97.63 256	96.95 291	88.86 302	84.91 325	93.13 318	78.32 307	96.74 318	88.70 284	81.81 323	94.09 330

v16		92.08 273	90.94 273	95.49 259	96.38 257	94.84 189	98.81 100	97.51 245	89.94 283	85.25 318	93.28 315	88.86 156	96.91 310	88.70 284	79.78 327	94.72 317

thres600view7		95.49 156	94.77 171	97.67 129	98.98 92	95.02 166	98.85 88	96.90 294	95.38 91	96.63 129	96.90 253	84.29 265	99.59 110	88.65 286	96.33 174	98.40 162

tfpn111		95.43 160	94.74 173	97.51 142	98.98 92	94.92 174	98.87 81	96.90 294	95.38 91	96.61 130	96.88 256	84.29 265	99.59 110	88.43 287	96.32 175	98.02 177

v15		91.94 275	90.77 277	95.43 265	96.31 269	94.83 191	98.77 111	97.50 248	89.92 284	85.13 319	93.08 320	88.76 167	96.86 312	88.40 288	79.10 331	94.61 322

V14		91.93 276	90.76 278	95.42 268	96.33 265	94.81 195	98.77 111	97.51 245	89.86 286	85.09 320	93.13 318	88.80 165	96.83 314	88.32 289	79.06 333	94.60 323

V9		91.91 277	90.73 279	95.45 262	96.32 268	94.80 196	98.77 111	97.50 248	89.81 287	85.03 322	93.08 320	88.76 167	96.86 312	88.24 290	79.03 334	94.69 319

v12		91.89 278	90.70 280	95.43 265	96.31 269	94.80 196	98.76 114	97.50 248	89.76 288	84.95 323	93.00 323	88.82 161	96.82 316	88.23 291	79.00 335	94.68 321

v13		91.88 279	90.69 281	95.43 265	96.33 265	94.78 201	98.75 115	97.50 248	89.68 291	84.93 324	92.98 324	88.84 159	96.83 314	88.14 292	79.09 332	94.69 319

conf200view11		95.40 165	94.70 175	97.50 147	98.98 92	94.92 174	98.87 81	96.90 294	95.38 91	96.61 130	96.88 256	84.29 265	99.56 119	88.11 293	96.29 177	98.02 177

thres100view900		95.38 166	94.70 175	97.41 151	98.98 92	94.92 174	98.87 81	96.90 294	95.38 91	96.61 130	96.88 256	84.29 265	99.56 119	88.11 293	96.29 177	97.76 185

tfpn200view9		95.32 173	94.62 178	97.43 150	98.94 97	94.98 170	98.68 133	96.93 292	95.33 99	96.55 136	96.53 271	84.23 270	99.56 119	88.11 293	96.29 177	97.76 185

thres400		95.38 166	94.62 178	97.65 131	98.94 97	94.98 170	98.68 133	96.93 292	95.33 99	96.55 136	96.53 271	84.23 270	99.56 119	88.11 293	96.29 177	98.40 162

thres200		95.25 175	94.57 180	97.28 157	98.81 114	94.92 174	98.20 194	97.11 278	95.24 106	96.54 138	96.22 284	84.58 257	99.53 126	87.93 297	96.50 166	97.39 198

EG-PatchMatch MVS		91.13 287	90.12 288	94.17 300	94.73 317	89.00 303	98.13 206	97.81 226	89.22 300	85.32 315	96.46 274	67.71 337	98.42 247	87.89 298	93.82 222	95.08 311

CR-MVSNet		94.76 200	94.15 200	96.59 205	97.00 221	93.43 243	94.96 327	97.56 236	92.46 216	96.93 114	96.24 280	88.15 184	97.88 292	87.38 299	96.65 160	98.46 159

v11		91.85 280	90.68 282	95.36 270	96.34 261	94.74 203	98.80 103	97.43 259	89.60 294	85.09 320	93.03 322	88.53 176	96.75 317	87.37 300	79.96 326	94.58 324

Patchmtry		93.22 260	92.35 261	95.84 248	96.77 234	93.09 252	94.66 333	97.56 236	87.37 310	92.90 252	96.24 280	88.15 184	97.90 288	87.37 300	90.10 262	96.53 276

test0.0.03 1		94.08 242	93.51 242	95.80 250	95.53 304	92.89 254	97.38 268	95.97 316	95.11 110	92.51 263	96.66 266	87.71 198	96.94 308	87.03 302	93.67 223	97.57 193

TinyColmap		92.31 269	91.53 268	94.65 289	96.92 226	89.75 291	96.92 290	96.68 305	90.45 271	89.62 292	97.85 167	76.06 318	98.81 205	86.74 303	92.51 242	95.41 307

MIMVSNet		93.26 259	92.21 263	96.41 224	97.73 177	93.13 251	95.65 322	97.03 283	91.27 262	94.04 220	96.06 288	75.33 320	97.19 305	86.56 304	96.23 184	98.92 136

TransMVSNet (Re)		92.67 265	91.51 269	96.15 237	96.58 244	94.65 204	98.90 74	96.73 302	90.86 268	89.46 294	97.86 165	85.62 241	98.09 278	86.45 305	81.12 324	95.71 302

DSMNet-mixed		92.52 267	92.58 258	92.33 311	94.15 320	82.65 330	98.30 186	94.26 340	89.08 301	92.65 257	95.73 295	85.01 251	95.76 329	86.24 306	97.76 143	98.59 154

testgi		93.06 263	92.45 260	94.88 282	96.43 252	89.90 289	98.75 115	97.54 241	95.60 81	91.63 276	97.91 161	74.46 326	97.02 307	86.10 307	93.67 223	97.72 189

YYNet1		90.70 293	89.39 294	94.62 290	94.79 316	90.65 283	97.20 281	97.46 255	87.54 309	72.54 340	95.74 294	86.51 219	96.66 322	86.00 308	86.76 309	96.54 275

MDA-MVSNet_test_wron		90.71 292	89.38 295	94.68 288	94.83 315	90.78 280	97.19 282	97.46 255	87.60 308	72.41 341	95.72 297	86.51 219	96.71 321	85.92 309	86.80 308	96.56 273

UnsupCasMVSNet_bld		87.17 307	85.12 310	93.31 306	91.94 328	88.77 304	94.92 329	98.30 166	84.30 326	82.30 328	90.04 336	63.96 342	97.25 304	85.85 310	74.47 342	93.93 333

EPNet_dtu		95.21 178	94.95 156	95.99 242	96.17 278	90.45 286	98.16 203	97.27 273	96.77 44	93.14 247	98.33 131	90.34 129	98.42 247	85.57 311	98.81 103	99.09 120
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet5		91.81 281	90.92 275	94.49 292	97.21 210	92.09 261	98.00 220	97.55 240	89.31 299	90.86 283	95.61 300	74.48 325	95.32 331	85.57 311	89.70 265	96.07 294

tfpnnormal		93.66 251	92.70 257	96.55 213	96.94 225	95.94 122	98.97 67	99.19 15	91.04 266	91.38 277	97.34 205	84.94 252	98.61 216	85.45 313	89.02 277	95.11 310

Patchmatch-test		94.42 223	93.68 232	96.63 200	97.60 183	91.76 267	94.83 331	97.49 254	89.45 296	94.14 215	97.10 224	88.99 148	98.83 203	85.37 314	98.13 130	99.29 99

PCF-MVS	93.45 11	94.68 209	93.43 245	98.42 85	98.62 129	96.77 88	95.48 323	98.20 182	84.63 325	93.34 240	98.32 132	88.55 175	99.81 53	84.80 315	98.96 94	98.68 148
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MDA-MVSNet-bldmvs		89.97 297	88.35 303	94.83 285	95.21 310	91.34 272	97.64 254	97.51 245	88.36 305	71.17 342	96.13 287	79.22 304	96.63 323	83.65 316	86.27 310	96.52 277

MVS-HIRNet		89.46 300	88.40 302	92.64 309	97.58 185	82.15 331	94.16 337	93.05 346	75.73 340	90.90 282	82.52 342	79.42 303	98.33 262	83.53 317	98.68 105	97.43 195

new-patchmatchnet		88.50 305	87.45 306	91.67 314	90.31 333	85.89 323	97.16 284	97.33 269	89.47 295	83.63 327	92.77 328	76.38 316	95.06 333	82.70 318	77.29 337	94.06 331

PAPM		94.95 189	94.00 210	97.78 119	97.04 220	95.65 143	96.03 316	98.25 174	91.23 263	94.19 212	97.80 174	91.27 116	98.86 200	82.61 319	97.61 146	98.84 140

LCM-MVSNet		78.70 315	76.24 319	86.08 325	77.26 352	71.99 345	94.34 335	96.72 303	61.62 346	76.53 337	89.33 337	33.91 355	92.78 341	81.85 320	74.60 341	93.46 334

new_pmnet		90.06 296	89.00 299	93.22 308	94.18 319	88.32 313	96.42 312	96.89 298	86.19 314	85.67 314	93.62 313	77.18 315	97.10 306	81.61 321	89.29 273	94.23 327

pmmvs3		86.67 309	84.86 311	92.11 313	88.16 337	87.19 321	96.63 304	94.75 336	79.88 336	87.22 305	92.75 329	66.56 339	95.20 332	81.24 322	76.56 339	93.96 332

N_pmnet		87.12 308	87.77 305	85.17 328	95.46 306	61.92 351	97.37 270	70.66 358	85.83 319	88.73 300	96.04 289	85.33 248	97.76 294	80.02 323	90.48 260	95.84 298

TAPA-MVS	93.98 7	95.35 170	94.56 181	97.74 120	99.13 82	94.83 191	98.33 179	98.64 113	86.62 312	96.29 156	98.61 103	94.00 75	99.29 143	80.00 324	99.41 79	99.09 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepMVS_CX				86.78 324	97.09 219	72.30 344		95.17 333	75.92 339	84.34 326	95.19 301	70.58 333	95.35 330	79.98 325	89.04 276	92.68 336

Anonymous20231206		91.66 283	91.10 271	93.33 305	94.02 322	87.35 319	98.58 146	97.26 274	90.48 269	90.16 288	96.31 278	83.83 279	96.53 324	79.36 326	89.90 264	96.12 292

test20.03		90.89 291	90.38 285	92.43 310	93.48 323	88.14 314	98.33 179	97.56 236	93.40 188	87.96 302	96.71 265	80.69 296	94.13 335	79.15 327	86.17 311	95.01 314

PatchT		93.06 263	91.97 265	96.35 228	96.69 240	92.67 255	94.48 334	97.08 279	86.62 312	97.08 105	92.23 334	87.94 190	97.90 288	78.89 328	96.69 158	98.49 158

MIMVSNet1		89.67 299	88.28 304	93.82 301	92.81 327	91.08 277	98.01 218	97.45 257	87.95 306	87.90 303	95.87 293	67.63 338	94.56 334	78.73 329	88.18 290	95.83 299

test_0402		91.32 285	90.27 287	94.48 293	96.60 243	91.12 276	98.50 163	97.22 276	86.10 316	88.30 301	96.98 242	77.65 312	97.99 284	78.13 330	92.94 239	94.34 326

OpenMVS_ROB	86.42 20	89.00 301	87.43 307	93.69 302	93.08 325	89.42 296	97.91 229	96.89 298	78.58 337	85.86 311	94.69 307	69.48 334	98.29 269	77.13 331	93.29 235	93.36 335

testus		88.91 302	89.08 297	88.40 320	91.39 329	76.05 338	96.56 307	96.48 310	89.38 298	89.39 295	95.17 303	70.94 332	93.56 338	77.04 332	95.41 202	95.61 304

RPMNet		92.52 267	91.17 270	96.59 205	97.00 221	93.43 243	94.96 327	97.26 274	82.27 331	96.93 114	92.12 335	86.98 213	97.88 292	76.32 333	96.65 160	98.46 159

Anonymous20231211		83.69 312	81.50 314	90.26 316	89.23 336	80.10 334	97.97 222	97.06 282	72.79 342	82.05 330	92.57 330	50.28 346	96.32 327	76.15 334	75.38 340	94.37 325

test2356		88.68 304	88.61 300	88.87 319	89.90 335	78.23 335	95.11 325	96.66 308	88.66 304	89.06 297	94.33 312	73.14 330	92.56 342	75.56 335	95.11 204	95.81 300

LP		91.12 288	89.99 290	94.53 291	96.35 260	88.70 306	93.86 338	97.35 265	84.88 323	90.98 281	94.77 306	84.40 264	97.43 301	75.41 336	91.89 250	97.47 194

PMMVS2		77.95 317	75.44 320	85.46 326	82.54 344	74.95 343	94.23 336	93.08 345	72.80 341	74.68 338	87.38 338	36.36 353	91.56 344	73.95 337	63.94 344	89.87 338

no-one		74.41 319	70.76 321	85.35 327	79.88 347	76.83 336	94.68 332	94.22 341	80.33 335	63.81 345	79.73 346	35.45 354	93.36 339	71.78 338	36.99 351	85.86 344

test1235678		86.26 310	85.81 309	87.62 322	86.97 340	75.00 342	96.55 309	96.32 313	86.08 317	81.32 332	92.98 324	73.10 331	92.05 343	71.64 339	87.32 300	95.81 300

test12356		83.47 313	83.37 313	83.78 329	84.43 343	70.09 347	95.12 324	95.60 327	82.98 327	78.89 335	92.43 333	64.99 340	91.41 345	70.36 340	85.55 316	89.82 339

FPMVS		77.62 318	77.14 316	79.05 333	79.25 348	60.97 352	95.79 320	95.94 317	65.96 343	67.93 344	94.40 309	37.73 352	88.88 348	68.83 341	88.46 287	87.29 341

1111		84.94 311	84.30 312	86.86 323	87.59 338	75.10 340	96.63 304	96.43 311	82.53 329	80.75 333	92.91 326	68.94 335	93.79 336	68.24 342	84.66 317	91.70 337

.test1245		73.05 320	76.31 318	63.27 341	87.59 338	75.10 340	96.63 304	96.43 311	82.53 329	80.75 333	92.91 326	68.94 335	93.79 336	68.24 342	12.72 354	20.91 354

Gipuma		78.40 316	76.75 317	83.38 330	95.54 303	80.43 333	79.42 350	97.40 262	64.67 344	73.46 339	80.82 345	45.65 349	93.14 340	66.32 344	87.43 298	76.56 350
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testmv		78.74 314	77.35 315	82.89 331	78.16 351	69.30 348	95.87 318	94.65 337	81.11 333	70.98 343	87.11 340	46.31 347	90.42 346	65.28 345	76.72 338	88.95 340

wuykxyi23d		63.73 327	58.86 329	78.35 334	67.62 354	67.90 349	86.56 346	87.81 353	58.26 347	42.49 353	70.28 351	11.55 360	85.05 349	63.66 346	41.50 347	82.11 347

PNet_i23d		67.70 323	65.07 324	75.60 335	78.61 349	59.61 354	89.14 344	88.24 352	61.83 345	52.37 349	80.89 344	18.91 357	84.91 350	62.70 347	52.93 346	82.28 346

ANet_high		69.08 321	65.37 323	80.22 332	65.99 355	71.96 346	90.91 343	90.09 349	82.62 328	49.93 351	78.39 347	29.36 356	81.75 351	62.49 348	38.52 350	86.95 343

PMVS	61.03 23	65.95 324	63.57 326	73.09 338	57.90 356	51.22 357	85.05 348	93.93 344	54.45 348	44.32 352	83.57 341	13.22 358	89.15 347	58.68 349	81.00 325	78.91 349
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	62.14 22	63.28 328	59.38 328	74.99 336	74.33 353	65.47 350	85.55 347	80.50 357	52.02 350	51.10 350	75.00 350	10.91 362	80.50 352	51.60 350	53.40 345	78.99 348
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		64.94 325	64.25 325	67.02 339	82.28 345	59.36 355	91.83 342	85.63 354	52.69 349	60.22 347	77.28 348	41.06 351	80.12 353	46.15 351	41.14 348	61.57 352

EMVS		64.07 326	63.26 327	66.53 340	81.73 346	58.81 356	91.85 341	84.75 355	51.93 351	59.09 348	75.13 349	43.32 350	79.09 354	42.03 352	39.47 349	61.69 351

wuyk23d		30.17 330	30.18 332	30.16 343	78.61 349	43.29 358	66.79 351	14.21 359	17.31 353	14.82 356	11.93 357	11.55 360	41.43 356	37.08 353	19.30 353	5.76 356

test123		20.95 333	23.72 334	12.64 344	13.54 359	8.19 359	96.55 309	6.13 361	7.48 355	16.74 355	37.98 354	12.97 359	6.05 357	16.69 354	5.43 356	23.68 353

testmvs		21.48 332	24.95 333	11.09 345	14.89 358	6.47 360	96.56 307	9.87 360	7.55 354	17.93 354	39.02 353	9.43 363	5.90 358	16.56 355	12.72 354	20.91 354

cdsmvs_eth3d_5k		23.98 331	31.98 331	0.00 346	0.00 360	0.00 361	0.00 352	98.59 116	0.00 356	0.00 357	98.61 103	90.60 126	0.00 359	0.00 356	0.00 357	0.00 357

pcd_1.5k_mvsjas		7.88 335	10.50 336	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	94.51 63	0.00 359	0.00 356	0.00 357	0.00 357

pcd1.5k->3k		39.42 329	41.78 330	32.35 342	96.17 278	0.00 361	0.00 352	98.54 126	0.00 356	0.00 357	0.00 358	87.78 197	0.00 359	0.00 356	93.56 227	97.06 210

sosnet-low-res		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

sosnet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

uncertanet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

Regformer		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

ab-mvs-re		8.20 334	10.94 335	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	98.43 118	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

uanet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

GSMVS																99.20 107

test_part2					99.63 21	99.18 1				99.27 7

test_part1								98.84 54				97.38 2			99.78 15	99.76 20

sam_mvs1												89.45 137				99.20 107

sam_mvs												88.99 148

MTGPA								98.74 80

test_post											31.83 355	88.83 160	98.91 192

patchmatchnet-post											95.10 304	89.42 138	98.89 196

MTMP								94.14 342

TEST9					99.31 50	98.50 15	97.92 226	98.73 85	92.63 211	97.74 84	98.68 97	96.20 15	99.80 60

test_8					99.29 58	98.44 17	97.89 234	98.72 87	92.98 202	97.70 87	98.66 100	96.20 15	99.80 60

agg_prior					99.30 55	98.38 20		98.72 87		97.57 96			99.81 53

test_prior4						98.01 44	97.86 237

test_prior				99.19 30	99.31 50	98.22 33		98.84 54					99.70 94			99.65 53

新几何2							97.64 254

旧先验1					99.29 58	97.48 62		98.70 94			99.09 55	95.56 38			99.47 72	99.61 59

原ACMM2							97.67 252

test222					99.23 73	97.17 75	97.40 266	98.66 108	88.68 303	98.05 63	98.96 72	94.14 72			99.53 68	99.61 59

segment_acmp												96.85 6

testdata1							97.32 276		96.34 59

test12				99.18 34	99.16 79	98.19 35		98.53 129		98.07 62		95.13 52	99.72 89		99.56 64	99.63 58

plane_prior7					97.42 197	94.63 206

plane_prior6					97.35 202	94.61 209						87.09 210

plane_prior4											98.28 134

plane_prior3						94.61 209			97.02 39	95.34 167

plane_prior2							98.80 103		97.28 21

plane_prior1					97.37 201

plane_prior						94.60 211	98.44 168		96.74 46						94.22 209

n2								0.00 362

nn								0.00 362

door-mid								94.37 339

test11								98.66 108

door								94.64 338

HQP5-MVS						94.25 224

HQP-NCC					97.20 211		98.05 214		96.43 54	94.45 188

ACMP_Plane					97.20 211		98.05 214		96.43 54	94.45 188

HQP4-MVS										94.45 188			98.96 185			96.87 230

HQP3-MVS								98.46 143							94.18 211

HQP2-MVS												86.75 216

NP-MVS					97.28 205	94.51 214					97.73 177

ACMMP++_ref															92.97 238

ACMMP++															93.61 226

Test By Simon												94.64 60

High-resolution multi-view benchmark