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	98.20 195	98.74 80	97.51 8	98.03 66	99.06 59	86.12 229	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	98.35 178	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	98.40 174	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	93.98 230	98.68 134	97.40 265	95.02 115	97.95 73	99.34 20	74.37 330	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	98.30 187	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	98.40 174	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	96.32 107	98.28 189	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	93.27 247	95.78 324	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	95.98 115	97.86 238	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	95.98 115	97.86 238	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	95.98 115	97.86 238	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	98.63 141	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	98.71 126	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	98.11 210	98.29 169	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	98.37 176	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	97.75 247	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	98.37 176	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 93	98.06 217	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 45	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	96.30 233	99.00 89	89.54 297	97.43 266	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	98.39 156	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 75	98.85 53	97.28 21	99.72 1	99.39 8	96.63 10	97.60 300	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	98.43 171	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 70	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	98.47 167	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	97.91 230	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	95.13 164	98.85 89	97.75 231	90.46 273	98.36 54	99.39 8	73.27 332	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	98.41 173	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	98.55 154	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 75	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	97.95 225	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 95	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	95.58 146	97.34 275	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 15	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		94.06 245	92.30 265	99.34 15	99.70 15	98.35 25	99.29 15	98.88 47	97.40 14	98.46 48	43.50 355	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	95.46 152	97.44 264	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 93	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 18	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 52	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 44	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	96.32 107	98.84 92	96.02 317	93.40 188	98.62 42	99.20 38	74.99 325	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 60	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 23	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 23	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	95.59 145	97.87 237	97.91 225	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	95.76 141	97.68 252	97.76 230	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 23	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	98.50 164	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	98.72 125	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	98.31 164	95.47 86	94.42 196	98.35 126	92.09 99	98.69 210	97.50 54	89.05 278	97.04 212

CSCG		97.85 54	97.74 48	98.20 94	99.67 18	95.16 162	99.22 29	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 17	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	93.78 235	98.31 185	98.19 184	94.01 147	94.47 187	98.27 137	92.08 100	98.46 240	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	98.45 168	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 72	99.23 12	95.13 109	95.51 166	97.32 207	85.73 242	98.91 192	97.33 59	89.55 271	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	89.25 144	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 67	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 61	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	95.91 135	98.63 141	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	96.65 198	95.58 303	94.42 217	99.00 63	98.55 125	95.73 76	93.21 243	98.38 123	83.45 284	98.63 215	97.09 64	94.00 218	96.91 224

EPNet		97.28 82	96.87 83	98.51 76	94.98 315	96.14 112	98.90 75	97.02 287	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	95.56 148	97.38 269	99.65 2	92.34 230	97.61 93	98.20 142	89.29 143	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	89.36 141	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 48	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	96.73 186	95.42 310	94.63 207	99.14 45	98.52 131	95.74 75	93.22 242	98.36 125	83.87 281	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	95.76 141	99.47 2	98.40 154	94.98 116	98.79 33	98.83 84	92.34 89	98.41 255	96.91 70	99.59 55	99.34 91

test_djsdf		96.00 125	95.69 127	96.93 178	95.72 299	95.49 151	99.47 2	98.40 154	94.98 116	94.58 183	97.86 165	89.16 147	98.41 255	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	97.80 243	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							97.80 243		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	95.38 154	99.33 13	98.31 164	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	96.92 180	95.84 295	95.08 166	99.16 43	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	95.35 157	98.26 191	99.26 8	94.28 139	97.94 74	97.46 196	92.74 86	99.81 53	96.88 75	93.32 233	96.20 293

PVSNet_Blended		97.38 78	97.12 72	98.14 97	99.25 67	95.35 157	97.28 279	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	97.37 271	98.43 151	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	94.56 214	98.05 215	99.71 1	93.57 177	97.09 104	98.91 79	88.17 185	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 64	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	97.02 171	98.77 116	93.76 236	97.79 245	98.50 138	95.45 87	96.94 113	99.09 55	87.87 197	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 29	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
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
test_normal		94.72 206	93.59 237	98.11 101	95.30 312	95.95 122	97.91 230	97.39 267	94.64 129	85.70 316	95.88 295	80.52 300	99.36 139	96.69 83	98.30 125	99.01 129

DI_MVS_plusplus_test		94.74 205	93.62 235	98.09 102	95.34 311	95.92 133	98.09 213	97.34 269	94.66 128	85.89 313	95.91 294	80.49 301	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	97.81 242	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	97.92 227	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	97.92 227	98.72 87	92.38 229	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	97.08 169	98.23 146	95.93 126	98.73 123	98.27 170	94.86 122	95.07 171	98.09 148	88.21 184	98.54 224	96.59 86	93.46 228	96.79 237

UGNet		96.78 101	96.30 105	98.19 96	98.24 145	95.89 137	98.88 81	98.93 36	97.39 16	96.81 124	97.84 168	82.60 287	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	98.80 104	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	98.71 126	98.97 29	95.67 78	94.84 176	98.24 140	80.36 302	98.67 213	96.46 91	87.32 303	96.96 216

sss		97.39 77	96.98 79	98.61 69	98.60 131	96.61 94	98.22 193	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	95.97 119	98.74 120	98.07 215	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	98.44 245	96.40 94	92.76 240	97.01 213

test9_res														96.39 95	99.57 58	99.69 38

test_part3							98.55 154		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	98.55 154	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	93.93 231	97.35 274	98.41 152	92.84 209	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	96.46 223	97.52 190	93.75 238	97.27 280	98.46 143	95.53 84	94.42 196	98.00 155	86.21 227	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	96.85 181	97.42 197	94.60 212	98.80 104	98.56 123	97.28 21	95.34 167	98.28 134	87.09 213	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 50	98.81 62	92.34 230	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	98.33 180	98.89 44	92.62 213	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	98.32 264	96.02 104	92.72 241	97.00 214

Vis-MVSNet		97.42 75	97.11 73	98.34 88	98.66 125	96.23 110	99.22 29	99.00 26	96.63 51	98.04 65	99.21 35	88.05 191	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	97.88 236	98.74 80	93.84 157	96.54 138	98.18 143	85.34 250	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	98.31 185	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	96.99 172	98.75 117	93.76 236	97.50 263	98.52 131	95.67 78	96.83 121	99.30 27	88.95 155	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	98.74 120	99.24 10	95.16 108	93.88 225	97.72 180	91.68 106	98.31 266	95.81 110	87.25 305	96.92 219

DU-MVS		95.42 162	94.76 172	97.40 153	96.53 247	96.97 79	98.66 139	98.99 28	95.43 88	93.88 225	97.69 181	88.57 175	98.31 266	95.81 110	87.25 305	96.92 219

UniMVSNet (Re)		95.78 134	95.19 145	97.58 138	96.99 223	97.47 63	98.79 109	99.18 16	95.60 81	93.92 224	97.04 237	91.68 106	98.48 235	95.80 112	87.66 300	96.79 237

cascas		94.63 213	93.86 220	96.93 178	96.91 228	94.27 224	96.00 320	98.51 133	85.55 323	94.54 184	96.23 285	84.20 275	98.87 198	95.80 112	96.98 155	97.66 192

Effi-MVS+-dtu		96.29 118	96.56 96	95.51 258	97.89 169	90.22 290	98.80 104	98.10 210	96.57 52	96.45 153	96.66 269	90.81 122	98.91 192	95.72 114	97.99 133	97.40 197

mvs-test1		96.60 105	96.68 93	96.37 227	97.89 169	91.81 266	98.56 152	98.10 210	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	96.47 220	97.46 193	93.54 241	98.99 64	98.54 126	94.67 126	94.36 198	98.77 90	85.39 247	99.11 166	95.71 116	94.15 213	96.76 240

LGP-MVS_train				96.47 220	97.46 193	93.54 241		98.54 126	94.67 126	94.36 198	98.77 90	85.39 247	99.11 166	95.71 116	94.15 213	96.76 240

旧先验2							97.57 260		91.30 261	98.67 39			99.80 60	95.70 118

LCM-MVSNet-Re		95.22 177	95.32 139	94.91 282	98.18 152	87.85 320	98.75 116	95.66 329	95.11 110	88.96 301	96.85 262	90.26 132	97.65 298	95.65 119	98.44 118	99.22 106

anonymousdsp		95.42 162	94.91 161	96.94 177	95.10 314	95.90 136	99.14 45	98.41 152	93.75 162	93.16 244	97.46 196	87.50 209	98.41 255	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	97.91 230	98.67 105	92.57 216	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	95.60 257	97.28 205	89.06 304	97.53 261	96.89 301	89.66 295	96.82 123	96.72 267	86.05 237	98.95 189	95.53 122	96.13 188	98.79 142

ACMM	93.85 9	95.69 140	95.38 135	96.61 204	97.61 182	93.84 234	98.91 74	98.44 147	95.25 104	94.28 206	98.47 116	86.04 239	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	96.43 224	97.67 178	93.48 243	98.73 123	98.44 147	94.94 121	92.53 261	98.53 110	84.50 266	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		90.61 297	88.50 304	96.95 176	90.08 337	95.57 147	97.69 251	98.06 217	93.02 200	76.55 339	92.48 335	61.18 346	98.44 245	95.45 125	91.98 247	96.84 233

Test4		92.21 273	90.34 289	97.82 117	92.83 329	95.87 139	97.94 226	98.05 220	94.50 134	82.12 332	94.48 311	59.54 347	98.54 224	95.39 126	98.22 126	99.06 125

TAMVS		97.02 92	96.79 86	97.70 126	98.06 159	95.31 159	98.52 159	98.31 164	93.95 152	97.05 109	98.61 103	93.49 78	98.52 231	95.33 127	97.81 140	99.29 99

BP-MVS														95.30 128

HQP-MVS		95.72 136	95.40 131	96.69 190	97.20 211	94.25 225	98.05 215	98.46 143	96.43 54	94.45 188	97.73 177	86.75 219	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	96.45 101	98.21 194	98.81 62	94.15 141	93.16 244	97.69 181	87.51 207	98.30 268	95.29 130	88.62 289	96.90 226

PatchFormer-LS_test		95.47 157	95.27 142	96.08 242	97.59 184	90.66 284	98.10 212	97.34 269	93.98 150	96.08 159	96.15 289	87.65 205	99.12 162	95.27 131	95.24 203	98.44 161

tpmrst		95.63 142	95.69 127	95.44 264	97.54 188	88.54 313	96.97 289	97.56 239	93.50 179	97.52 98	96.93 253	89.49 138	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	95.98 115	98.20 195	98.33 163	93.67 174	96.95 111	98.49 114	93.54 77	98.42 248	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	96.76 185	96.16 282	94.63 207	98.43 171	98.39 156	96.64 50	95.02 173	98.78 88	85.15 252	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	95.92 133	98.28 189	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	96.07 114	97.98 222	98.21 180	97.24 27	97.13 103	98.93 76	86.88 218	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 79	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 89	98.90 42	84.80 327	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 88	98.65 112	93.30 193	93.27 241	98.27 137	84.85 257	98.87 198	94.82 139	91.26 258	96.96 216

MG-MVS		97.81 55	97.60 51	98.44 82	99.12 83	95.97 119	97.75 247	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	96.36 228	97.93 166	93.70 240	98.12 208	98.27 170	93.70 169	95.07 171	99.02 61	92.23 94	98.54 224	94.68 141	93.46 228	96.84 233

IterMVS-LS		95.46 158	95.21 144	96.22 236	98.12 155	93.72 239	98.32 184	98.13 198	93.71 167	94.26 207	97.31 208	92.24 93	98.10 277	94.63 142	90.12 263	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	95.55 150	98.19 199	98.59 116	93.47 180	92.03 274	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	96.36 105	99.19 35	97.97 222	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	96.53 247	97.22 73	98.74 120	98.95 33	94.96 118	89.25 299	97.69 181	89.32 142	98.18 274	94.59 145	87.40 302	96.92 219

IB-MVS	91.98 17	93.27 260	91.97 268	97.19 160	97.47 192	93.41 246	97.09 287	95.99 318	93.32 191	92.47 264	95.73 298	78.06 311	99.53 126	94.59 145	82.98 322	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	96.20 237	97.35 202	90.79 281	98.34 179	96.57 312	92.91 205	95.33 169	96.44 279	82.00 289	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	98.14 205	98.21 180	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		94.35 227	93.81 222	95.96 244	96.20 277	94.05 229	98.61 144	96.67 309	91.44 252	93.85 227	97.60 189	88.57 175	98.14 275	94.39 149	86.93 308	95.68 306

AdaColmap		97.15 88	96.70 90	98.48 79	99.16 79	96.69 91	98.01 219	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	96.37 104	98.18 203	98.10 210	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	96.83 182	96.72 239	95.56 148	99.11 51	98.95 33	93.89 154	92.42 266	97.90 162	87.19 212	98.12 276	94.32 152	88.21 292	96.82 236

CNLPA		97.45 72	97.03 77	98.73 62	99.05 84	97.44 65	98.07 214	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	95.36 155		98.68 98	91.89 241	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	96.96 175	99.17 78	92.05 263	96.08 316	98.68 98	93.69 170	97.75 83	97.80 174	88.86 158	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	95.94 123	97.71 249	98.07 215	92.10 236	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	96.45 252	96.36 105	99.03 61	99.03 24	95.04 114	93.58 232	97.93 160	88.27 183	98.03 282	94.13 157	86.90 310	96.95 218

API-MVS		97.41 76	97.25 67	97.91 111	98.70 121	96.80 86	98.82 95	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	96.31 109	98.14 205	98.76 76	92.41 227	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		94.54 219	94.14 201	95.75 254	96.55 246	91.65 271	98.11 210	98.44 147	94.96 118	94.22 210	97.90 162	79.18 308	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	94.95 174	98.55 154	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						84.26 328	96.89 298		90.97 269	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	96.14 112	98.53 158	98.23 178	90.10 281	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	93.26 310	98.89 107	82.06 335	98.33 180	98.06 217	90.30 277	96.56 134	99.26 30	87.09 213	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	96.41 103	98.63 141	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	94.55 218	93.95 215	96.34 231	97.63 180	93.26 248	98.81 101	98.49 142	93.43 181	89.74 294	98.53 110	81.91 290	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	98.66 139	98.68 98	92.40 228	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	95.46 152	99.20 33	98.30 167	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 73	98.90 42	92.83 210	95.99 163	99.37 13	92.12 98	99.87 38	93.67 169	99.57 58	98.97 131

PS-CasMVS		94.67 211	93.99 213	96.71 187	96.68 241	95.26 160	99.13 48	99.03 24	93.68 172	92.33 267	97.95 158	85.35 249	98.10 277	93.59 171	88.16 294	96.79 237

CVMVSNet		95.43 160	96.04 113	93.57 306	97.93 166	83.62 329	98.12 208	98.59 116	95.68 77	96.56 134	99.02 61	87.51 207	97.51 303	93.56 172	97.44 148	99.60 62

OurMVSNet-221017-0		94.21 233	94.00 211	94.85 285	95.60 302	89.22 302	98.89 79	97.43 262	95.29 102	92.18 271	98.52 113	82.86 286	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 32	98.97 29	89.96 284	91.14 282	99.05 60	86.64 221	99.92 15	93.38 174	99.47 72	97.73 188

无先验							97.58 259	98.72 87	91.38 255				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	98.19 199	98.68 98	90.14 280	98.01 69	98.97 68	94.80 59	99.87 38	93.36 175	99.46 75	99.61 59

gm-plane-assit					95.88 293	87.47 321			89.74 293		96.94 249		99.19 156	93.32 177

WR-MVS_H		95.05 183	94.46 185	96.81 183	96.86 231	95.82 140	99.24 21	99.24 10	93.87 156	92.53 261	96.84 263	90.37 128	98.24 272	93.24 178	87.93 295	96.38 286

tpm		94.13 241	93.80 223	95.12 277	96.50 249	87.91 319	97.44 264	95.89 322	92.62 213	96.37 155	96.30 282	84.13 276	98.30 268	93.24 178	91.66 253	99.14 117

Fast-Effi-MVS+-dtu		95.87 130	95.85 118	95.91 246	97.74 176	91.74 270	98.69 130	98.15 195	95.56 83	94.92 174	97.68 184	88.98 153	98.79 207	93.19 180	97.78 142	97.20 208

pmmvs5		93.65 254	92.97 254	95.68 255	95.49 306	92.37 259	98.20 195	97.28 275	89.66 295	92.58 259	97.26 210	82.14 288	98.09 279	93.18 181	90.95 259	96.58 269

TESTMET0.1,1		94.18 237	93.69 232	95.63 256	96.92 226	89.12 303	96.91 293	94.78 338	93.17 195	94.88 175	96.45 278	78.52 309	98.92 191	93.09 182	98.50 115	98.85 138

test-LLR		95.10 182	94.87 163	95.80 251	96.77 234	89.70 294	96.91 293	95.21 333	95.11 110	94.83 178	95.72 300	87.71 201	98.97 182	93.06 183	98.50 115	98.72 144

test-mter		94.08 243	93.51 243	95.80 251	96.77 234	89.70 294	96.91 293	95.21 333	92.89 206	94.83 178	95.72 300	77.69 313	98.97 182	93.06 183	98.50 115	98.72 144

BH-untuned		95.95 127	95.72 122	96.65 198	98.55 134	92.26 260	98.23 192	97.79 229	93.73 165	94.62 182	98.01 154	88.97 154	99.00 181	93.04 185	98.51 114	98.68 148

EPMVS		94.99 185	94.48 183	96.52 216	97.22 209	91.75 269	97.23 281	91.66 350	94.11 142	97.28 100	96.81 264	85.70 243	98.84 201	93.04 185	97.28 150	98.97 131

pmmvs4		94.69 207	93.99 213	96.81 183	95.74 297	95.94 123	97.40 267	97.67 234	90.42 275	93.37 239	97.59 190	89.08 149	98.20 273	92.97 187	91.67 252	96.30 290

v6		94.83 194	94.21 196	96.69 190	96.36 259	94.85 181	98.87 82	98.11 205	92.46 217	94.44 194	97.05 236	88.76 169	98.57 222	92.95 188	88.92 281	96.65 260

v1neww		94.83 194	94.22 194	96.68 193	96.39 255	94.85 181	98.87 82	98.11 205	92.45 222	94.45 188	97.06 232	88.82 163	98.54 224	92.93 189	88.91 282	96.65 260

v7new		94.83 194	94.22 194	96.68 193	96.39 255	94.85 181	98.87 82	98.11 205	92.45 222	94.45 188	97.06 232	88.82 163	98.54 224	92.93 189	88.91 282	96.65 260

v2v482		94.69 207	94.03 208	96.65 198	96.17 279	94.79 200	98.67 137	98.08 214	92.72 211	94.00 222	97.16 216	87.69 204	98.45 242	92.91 191	88.87 284	96.72 245

Fast-Effi-MVS+		96.28 120	95.70 126	98.03 107	98.29 143	95.97 119	98.58 147	98.25 175	91.74 245	95.29 170	97.23 213	91.03 121	99.15 158	92.90 192	97.96 134	98.97 131

V42		94.78 200	94.14 201	96.70 189	96.33 266	95.22 161	98.97 68	98.09 213	92.32 232	94.31 202	97.06 232	88.39 181	98.55 223	92.90 192	88.87 284	96.34 288

DP-MVS		96.59 107	95.93 116	98.57 71	99.34 42	96.19 111	98.70 129	98.39 156	89.45 299	94.52 185	99.35 19	91.85 104	99.85 43	92.89 194	98.88 97	99.68 44

TDRefinement		91.06 292	89.68 295	95.21 274	85.35 345	91.49 272	98.51 163	97.07 283	91.47 250	88.83 302	97.84 168	77.31 317	99.09 170	92.79 195	77.98 339	95.04 315

ACMH+	92.99 14	94.30 229	93.77 226	95.88 248	97.81 173	92.04 264	98.71 126	98.37 159	93.99 149	90.60 289	98.47 116	80.86 297	99.05 173	92.75 196	92.40 243	96.55 274

divwei89l23v2f112		94.76 201	94.12 204	96.67 196	96.28 272	94.85 181	98.69 130	98.12 200	92.44 224	94.29 205	96.94 249	88.85 160	98.48 235	92.67 197	88.79 288	96.67 255

v1		94.75 203	94.11 205	96.69 190	96.27 274	94.87 179	98.69 130	98.12 200	92.43 225	94.32 201	96.94 249	88.71 172	98.54 224	92.66 198	88.84 287	96.67 255

v1141		94.75 203	94.11 205	96.67 196	96.27 274	94.86 180	98.69 130	98.12 200	92.43 225	94.31 202	96.94 249	88.78 168	98.48 235	92.63 199	88.85 286	96.67 255

test_post1							96.68 306				30.43 359	87.85 198	98.69 210	92.59 200

v148		94.29 230	93.76 228	95.91 246	96.10 283	92.93 254	98.58 147	97.97 222	92.59 215	93.47 238	96.95 247	88.53 178	98.32 264	92.56 201	87.06 307	96.49 281

PEN-MVS		94.42 224	93.73 230	96.49 218	96.28 272	94.84 190	99.17 36	99.00 26	93.51 178	92.23 269	97.83 171	86.10 236	97.90 290	92.55 202	86.92 309	96.74 242

Patchmatch-RL test		91.49 287	90.85 279	93.41 307	91.37 333	84.40 327	92.81 342	95.93 321	91.87 243	87.25 307	94.87 308	88.99 150	96.53 327	92.54 203	82.00 324	99.30 97

IterMVS		94.09 242	93.85 221	94.80 288	97.99 163	90.35 289	97.18 284	98.12 200	93.68 172	92.46 265	97.34 205	84.05 277	97.41 305	92.51 204	91.33 255	96.62 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
semantic-postprocess				94.85 285	97.98 165	90.56 287		98.11 205	93.75 162	92.58 259	97.48 195	83.91 279	97.41 305	92.48 205	91.30 256	96.58 269

tpm2		94.19 235	93.76 228	95.46 262	97.23 208	89.04 305	97.31 278	96.85 304	87.08 314	96.21 157	96.79 265	83.75 283	98.74 209	92.43 206	96.23 184	98.59 154

PVSNet_0	88.72 19	91.28 289	90.03 292	95.00 280	97.99 163	87.29 323	94.84 333	98.50 138	92.06 237	89.86 293	95.19 304	79.81 304	99.39 137	92.27 207	69.79 346	98.33 170

gg-mvs-nofinetune		92.21 273	90.58 287	97.13 164	96.75 237	95.09 165	95.85 322	89.40 353	85.43 324	94.50 186	81.98 346	80.80 298	98.40 261	92.16 208	98.33 123	97.88 183

pm-mvs1		93.94 248	93.06 252	96.59 206	96.49 250	95.16 162	98.95 70	98.03 221	92.32 232	91.08 283	97.84 168	84.54 265	98.41 255	92.16 208	86.13 316	96.19 294

K. test v3		92.55 269	91.91 270	94.48 296	95.64 301	89.24 301	99.07 57	94.88 337	94.04 146	86.78 309	97.59 190	77.64 316	97.64 299	92.08 210	89.43 273	96.57 271

GBi-Net		94.49 220	93.80 223	96.56 211	98.21 147	95.00 168	98.82 95	98.18 187	92.46 217	94.09 217	97.07 229	81.16 292	97.95 286	92.08 210	92.14 244	96.72 245

test1		94.49 220	93.80 223	96.56 211	98.21 147	95.00 168	98.82 95	98.18 187	92.46 217	94.09 217	97.07 229	81.16 292	97.95 286	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	98.56 152	98.26 174	93.67 174	94.09 217	97.10 225	84.25 272	98.01 283	92.08 210	92.14 244	96.70 249

Anonymous20240521		94.80 199	94.03 208	97.11 166	96.56 245	96.46 100	99.30 14	98.44 147	92.86 208	91.21 280	97.01 241	89.59 137	98.58 221	92.03 214	89.23 276	96.30 290

PatchmatchNet		95.71 138	95.52 130	96.29 234	97.58 185	90.72 283	96.84 302	97.52 245	94.06 145	97.08 105	96.96 246	89.24 145	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 23	98.93 36	89.76 291	93.11 248	99.02 61	89.11 148	99.93 9	91.99 216	99.62 50	99.34 91

新几何1				99.16 37	99.34 42	98.01 44		98.69 95	90.06 282	98.13 59	98.95 74	94.60 61	99.89 29	91.97 217	99.47 72	99.59 64

v7		94.69 207	94.04 207	96.62 203	96.41 254	94.79 200	98.78 111	98.13 198	91.89 241	94.30 204	97.16 216	88.13 188	98.45 242	91.96 218	89.65 268	96.61 265

MDTV_nov1_ep13			95.40 131		97.48 191	88.34 315	96.85 301	97.29 274	93.74 164	97.48 99	97.26 210	89.18 146	99.05 173	91.92 219	97.43 149

EU-MVSNet		93.66 252	94.14 201	92.25 315	95.96 289	83.38 330	98.52 159	98.12 200	94.69 124	92.61 258	98.13 146	87.36 211	96.39 329	91.82 220	90.00 265	96.98 215

GA-MVS		94.81 198	94.03 208	97.14 163	97.15 216	93.86 233	96.76 304	97.58 238	94.00 148	94.76 181	97.04 237	80.91 295	98.48 235	91.79 221	96.25 183	99.09 120

tfpn1000		95.72 136	95.11 147	97.58 138	99.00 89	95.73 143	99.24 21	95.49 331	94.08 144	96.87 120	97.45 198	85.81 241	99.30 141	91.78 222	96.22 186	97.71 190

PatchMatch-RL		96.59 107	96.03 114	98.27 90	99.31 50	96.51 98	97.91 230	99.06 21	93.72 166	96.92 116	98.06 150	88.50 180	99.65 101	91.77 223	99.00 93	98.66 150

v1144		94.59 216	93.92 216	96.60 205	96.21 276	94.78 202	98.59 145	98.14 197	91.86 244	94.21 211	97.02 239	87.97 192	98.41 255	91.72 224	89.57 269	96.61 265

v8		94.47 222	93.77 226	96.57 210	96.36 259	94.83 192	99.05 58	98.19 184	91.92 240	93.16 244	96.97 245	88.82 163	98.48 235	91.69 225	87.79 298	96.39 285

testdata2													99.89 29	91.65 226

BH-w/o		95.38 166	95.08 149	96.26 235	98.34 140	91.79 267	97.70 250	97.43 262	92.87 207	94.24 209	97.22 214	88.66 173	98.84 201	91.55 227	97.70 145	98.16 174

tfpn_ndepth		95.53 151	94.90 162	97.39 156	98.96 96	95.88 138	99.05 58	95.27 332	93.80 160	96.95 111	96.93 253	85.53 245	99.40 135	91.54 228	96.10 189	96.89 227

v52		94.18 237	93.52 241	96.13 240	95.95 290	94.29 223	99.23 23	98.21 180	91.42 253	92.84 253	96.89 256	87.85 198	98.53 230	91.51 229	87.81 296	95.57 309

V4		94.18 237	93.52 241	96.13 240	95.89 292	94.31 222	99.23 23	98.22 179	91.42 253	92.82 254	96.89 256	87.93 194	98.52 231	91.51 229	87.81 296	95.58 308

LF4IMVS		93.14 265	92.79 257	94.20 301	95.88 293	88.67 310	97.66 254	97.07 283	93.81 159	91.71 276	97.65 185	77.96 312	98.81 205	91.47 231	91.92 249	95.12 312

JIA-IIPM		93.35 257	92.49 262	95.92 245	96.48 251	90.65 285	95.01 329	96.96 293	85.93 321	96.08 159	87.33 342	87.70 203	98.78 208	91.35 232	95.58 201	98.34 169

Patchmatch-test1		95.32 173	94.97 155	96.35 229	97.67 178	91.29 275	97.33 276	97.60 237	94.68 125	96.92 116	96.95 247	83.97 278	98.50 234	91.33 233	98.32 124	99.25 103

FMVSNet2		94.47 222	93.61 236	97.04 170	98.21 147	96.43 102	98.79 109	98.27 170	92.46 217	93.50 237	97.09 227	81.16 292	98.00 284	91.09 234	91.93 248	96.70 249

v144192		94.39 226	93.70 231	96.48 219	96.06 285	94.35 221	98.58 147	98.16 194	91.45 251	94.33 200	97.02 239	87.50 209	98.45 242	91.08 235	89.11 277	96.63 263

tpmvs		94.60 214	94.36 190	95.33 273	97.46 193	88.60 311	96.88 299	97.68 233	91.29 262	93.80 229	96.42 280	88.58 174	99.24 146	91.06 236	96.04 196	98.17 173

LTVRE_ROB	92.95 15	94.60 214	93.90 218	96.68 193	97.41 200	94.42 217	98.52 159	98.59 116	91.69 246	91.21 280	98.35 126	84.87 256	99.04 177	91.06 236	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	97.36 273	98.57 122	93.33 190	96.67 128	97.57 192	94.30 70	99.56 119	91.05 238	98.59 111	99.47 80

SixPastTwentyTwo		93.34 258	92.86 255	94.75 289	95.67 300	89.41 300	98.75 116	96.67 309	93.89 154	90.15 292	98.25 139	80.87 296	98.27 271	90.90 239	90.64 260	96.57 271

COLMAP_ROB	93.27 12	95.33 172	94.87 163	96.71 187	99.29 58	93.24 249	98.58 147	98.11 205	89.92 287	93.57 233	99.10 51	86.37 225	99.79 72	90.78 240	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		91.77 285	90.63 286	95.17 276	94.69 321	91.24 276	98.67 137	97.92 224	86.14 318	89.62 295	97.56 193	75.79 322	98.34 262	90.75 241	84.56 321	95.94 300

BH-RMVSNet		95.92 129	95.32 139	97.69 127	98.32 142	94.64 206	98.19 199	97.45 260	94.56 131	96.03 161	98.61 103	85.02 253	99.12 162	90.68 242	99.06 91	99.30 97

v748		93.75 251	93.06 252	95.82 250	95.73 298	92.64 257	99.25 20	98.24 177	91.60 248	92.22 270	96.52 276	87.60 206	98.46 240	90.64 243	85.72 317	96.36 287

tpmp4_e23		93.91 249	93.42 248	95.38 270	97.62 181	88.59 312	97.52 262	97.34 269	87.94 310	94.17 214	96.79 265	82.91 285	99.05 173	90.62 244	95.91 197	98.50 157

DTE-MVSNet		93.98 247	93.26 251	96.14 239	96.06 285	94.39 219	99.20 33	98.86 52	93.06 198	91.78 275	97.81 173	85.87 240	97.58 301	90.53 245	86.17 314	96.46 284

conf0.01		95.56 149	94.84 165	97.72 121	98.90 99	95.93 126	99.17 36	95.70 323	93.42 182	96.50 145	97.16 216	86.12 229	99.22 149	90.51 246	96.06 190	98.02 177

conf0.002		95.56 149	94.84 165	97.72 121	98.90 99	95.93 126	99.17 36	95.70 323	93.42 182	96.50 145	97.16 216	86.12 229	99.22 149	90.51 246	96.06 190	98.02 177

thresconf0.02		95.50 152	94.84 165	97.51 142	98.90 99	95.93 126	99.17 36	95.70 323	93.42 182	96.50 145	97.16 216	86.12 229	99.22 149	90.51 246	96.06 190	97.37 200

tfpn_n400		95.50 152	94.84 165	97.51 142	98.90 99	95.93 126	99.17 36	95.70 323	93.42 182	96.50 145	97.16 216	86.12 229	99.22 149	90.51 246	96.06 190	97.37 200

tfpnconf		95.50 152	94.84 165	97.51 142	98.90 99	95.93 126	99.17 36	95.70 323	93.42 182	96.50 145	97.16 216	86.12 229	99.22 149	90.51 246	96.06 190	97.37 200

tfpnview11		95.50 152	94.84 165	97.51 142	98.90 99	95.93 126	99.17 36	95.70 323	93.42 182	96.50 145	97.16 216	86.12 229	99.22 149	90.51 246	96.06 190	97.37 200

v10		94.29 230	93.55 239	96.51 217	96.39 255	94.80 197	98.99 64	98.19 184	91.35 258	93.02 250	96.99 243	88.09 189	98.41 255	90.50 252	88.41 291	96.33 289

ambc				89.49 321	86.66 344	75.78 342	92.66 343	96.72 306		86.55 311	92.50 334	46.01 351	97.90 290	90.32 253	82.09 323	94.80 318

lessismore_v0				94.45 299	94.93 317	88.44 314		91.03 351		86.77 310	97.64 187	76.23 320	98.42 248	90.31 254	85.64 318	96.51 279

v1192		94.32 228	93.58 238	96.53 215	96.10 283	94.45 216	98.50 164	98.17 192	91.54 249	94.19 212	97.06 232	86.95 217	98.43 247	90.14 255	89.57 269	96.70 249

MVS		94.67 211	93.54 240	98.08 103	96.88 230	96.56 96	98.19 199	98.50 138	78.05 341	92.69 256	98.02 152	91.07 120	99.63 106	90.09 256	98.36 122	98.04 176

ADS-MVSNet2		94.58 217	94.40 189	95.11 278	98.00 161	88.74 308	96.04 317	97.30 273	90.15 278	96.47 151	96.64 271	87.89 195	97.56 302	90.08 257	97.06 152	99.02 126

ADS-MVSNet		95.00 184	94.45 187	96.63 201	98.00 161	91.91 265	96.04 317	97.74 232	90.15 278	96.47 151	96.64 271	87.89 195	98.96 185	90.08 257	97.06 152	99.02 126

MSDG		95.93 128	95.30 141	97.83 115	98.90 99	95.36 155	96.83 303	98.37 159	91.32 260	94.43 195	98.73 94	90.27 131	99.60 109	90.05 259	98.82 102	98.52 156

v1921920		94.20 234	93.47 245	96.40 226	95.98 288	94.08 228	98.52 159	98.15 195	91.33 259	94.25 208	97.20 215	86.41 224	98.42 248	90.04 260	89.39 274	96.69 254

dp		94.15 240	93.90 218	94.90 283	97.31 204	86.82 325	96.97 289	97.19 280	91.22 266	96.02 162	96.61 273	85.51 246	99.02 180	90.00 261	94.30 206	98.85 138

CMPMVS	66.06 21	89.70 301	89.67 296	89.78 320	93.19 327	76.56 340	97.00 288	98.35 161	80.97 337	81.57 334	97.75 176	74.75 327	98.61 216	89.85 262	93.63 225	94.17 331
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testpf		88.74 306	89.09 299	87.69 324	95.78 296	83.16 332	84.05 352	94.13 346	85.22 325	90.30 290	94.39 313	74.92 326	95.80 331	89.77 263	93.28 236	84.10 348

TR-MVS		94.94 191	94.20 197	97.17 162	97.75 175	94.14 227	97.59 258	97.02 287	92.28 234	95.75 165	97.64 187	83.88 280	98.96 185	89.77 263	96.15 187	98.40 162

MS-PatchMatch		93.84 250	93.63 234	94.46 298	96.18 278	89.45 298	97.76 246	98.27 170	92.23 235	92.13 272	97.49 194	79.50 305	98.69 210	89.75 265	99.38 82	95.25 311

ITE_SJBPF				95.44 264	97.42 197	91.32 274		97.50 251	95.09 113	93.59 231	98.35 126	81.70 291	98.88 197	89.71 266	93.39 232	96.12 295

MVP-Stereo		94.28 232	93.92 216	95.35 272	94.95 316	92.60 258	97.97 223	97.65 235	91.61 247	90.68 288	97.09 227	86.32 226	98.42 248	89.70 267	99.34 84	95.02 316
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
AllTest		95.24 176	94.65 177	96.99 172	99.25 67	93.21 250	98.59 145	98.18 187	91.36 256	93.52 235	98.77 90	84.67 258	99.72 89	89.70 267	97.87 137	98.02 177

TestCases				96.99 172	99.25 67	93.21 250		98.18 187	91.36 256	93.52 235	98.77 90	84.67 258	99.72 89	89.70 267	97.87 137	98.02 177

GG-mvs-BLEND				96.59 206	96.34 262	94.98 171	96.51 314	88.58 354		93.10 249	94.34 314	80.34 303	98.05 281	89.53 270	96.99 154	96.74 242

USDC		93.33 259	92.71 258	95.21 274	96.83 233	90.83 280	96.91 293	97.50 251	93.84 157	90.72 287	98.14 145	77.69 313	98.82 204	89.51 271	93.21 237	95.97 299

v7n		94.19 235	93.43 246	96.47 220	95.90 291	94.38 220	99.26 18	98.34 162	91.99 238	92.76 255	97.13 224	88.31 182	98.52 231	89.48 272	87.70 299	96.52 277

PM-MVS		87.77 309	86.55 311	91.40 318	91.03 335	83.36 331	96.92 291	95.18 335	91.28 263	86.48 312	93.42 317	53.27 348	96.74 321	89.43 273	81.97 325	94.11 332

FMVSNet1		93.19 264	92.07 267	96.56 211	97.54 188	95.00 168	98.82 95	98.18 187	90.38 276	92.27 268	97.07 229	73.68 331	97.95 286	89.36 274	91.30 256	96.72 245

tpm cat1		93.36 256	92.80 256	95.07 279	97.58 185	87.97 318	96.76 304	97.86 227	82.17 335	93.53 234	96.04 292	86.13 228	99.13 161	89.24 275	95.87 198	98.10 175

UnsupCasMVSNet_eth		90.99 293	89.92 294	94.19 302	94.08 324	89.83 292	97.13 286	98.67 105	93.69 170	85.83 315	96.19 288	75.15 324	96.74 321	89.14 276	79.41 333	96.00 298

v1240		94.06 245	93.29 250	96.34 231	96.03 287	93.90 232	98.44 169	98.17 192	91.18 267	94.13 216	97.01 241	86.05 237	98.42 248	89.13 277	89.50 272	96.70 249

view600		95.60 145	94.93 157	97.62 132	99.05 84	94.85 181	99.09 53	97.01 289	95.36 95	96.52 140	97.37 201	84.55 261	99.59 110	89.07 278	96.39 169	98.40 162

view800		95.60 145	94.93 157	97.62 132	99.05 84	94.85 181	99.09 53	97.01 289	95.36 95	96.52 140	97.37 201	84.55 261	99.59 110	89.07 278	96.39 169	98.40 162

conf0.05thres1000		95.60 145	94.93 157	97.62 132	99.05 84	94.85 181	99.09 53	97.01 289	95.36 95	96.52 140	97.37 201	84.55 261	99.59 110	89.07 278	96.39 169	98.40 162

tfpn		95.60 145	94.93 157	97.62 132	99.05 84	94.85 181	99.09 53	97.01 289	95.36 95	96.52 140	97.37 201	84.55 261	99.59 110	89.07 278	96.39 169	98.40 162

tmp_tt		68.90 325	66.97 325	74.68 340	50.78 360	59.95 356	87.13 348	83.47 359	38.80 355	62.21 349	96.23 285	64.70 344	76.91 358	88.91 282	30.49 355	87.19 345

v18		92.10 275	90.97 275	95.50 259	96.34 262	94.85 181	98.82 95	97.52 245	89.99 283	85.31 320	93.26 319	88.90 157	96.92 312	88.82 283	79.77 331	94.73 319

v17		92.08 276	90.94 276	95.48 261	96.34 262	94.83 192	98.81 101	97.52 245	89.95 285	85.32 318	93.24 320	88.91 156	96.91 313	88.76 284	79.63 332	94.71 321

pmmvs-eth3d		90.36 298	89.05 301	94.32 300	91.10 334	92.12 261	97.63 257	96.95 294	88.86 305	84.91 328	93.13 321	78.32 310	96.74 321	88.70 285	81.81 326	94.09 333

v16		92.08 276	90.94 276	95.49 260	96.38 258	94.84 190	98.81 101	97.51 248	89.94 286	85.25 321	93.28 318	88.86 158	96.91 313	88.70 285	79.78 330	94.72 320

thres600view7		95.49 156	94.77 171	97.67 129	98.98 92	95.02 167	98.85 89	96.90 297	95.38 91	96.63 129	96.90 255	84.29 268	99.59 110	88.65 287	96.33 174	98.40 162

tfpn111		95.43 160	94.74 173	97.51 142	98.98 92	94.92 175	98.87 82	96.90 297	95.38 91	96.61 130	96.88 258	84.29 268	99.59 110	88.43 288	96.32 175	98.02 177

v15		91.94 278	90.77 280	95.43 266	96.31 270	94.83 192	98.77 112	97.50 251	89.92 287	85.13 322	93.08 323	88.76 169	96.86 315	88.40 289	79.10 334	94.61 325

V14		91.93 279	90.76 281	95.42 269	96.33 266	94.81 196	98.77 112	97.51 248	89.86 289	85.09 323	93.13 321	88.80 167	96.83 317	88.32 290	79.06 336	94.60 326

V9		91.91 280	90.73 282	95.45 263	96.32 269	94.80 197	98.77 112	97.50 251	89.81 290	85.03 325	93.08 323	88.76 169	96.86 315	88.24 291	79.03 337	94.69 322

v12		91.89 281	90.70 283	95.43 266	96.31 270	94.80 197	98.76 115	97.50 251	89.76 291	84.95 326	93.00 326	88.82 163	96.82 319	88.23 292	79.00 338	94.68 324

v13		91.88 282	90.69 284	95.43 266	96.33 266	94.78 202	98.75 116	97.50 251	89.68 294	84.93 327	92.98 327	88.84 161	96.83 317	88.14 293	79.09 335	94.69 322

conf200view11		95.40 165	94.70 175	97.50 147	98.98 92	94.92 175	98.87 82	96.90 297	95.38 91	96.61 130	96.88 258	84.29 268	99.56 119	88.11 294	96.29 177	98.02 177

thres100view900		95.38 166	94.70 175	97.41 151	98.98 92	94.92 175	98.87 82	96.90 297	95.38 91	96.61 130	96.88 258	84.29 268	99.56 119	88.11 294	96.29 177	97.76 185

tfpn200view9		95.32 173	94.62 178	97.43 150	98.94 97	94.98 171	98.68 134	96.93 295	95.33 99	96.55 136	96.53 274	84.23 273	99.56 119	88.11 294	96.29 177	97.76 185

thres400		95.38 166	94.62 178	97.65 131	98.94 97	94.98 171	98.68 134	96.93 295	95.33 99	96.55 136	96.53 274	84.23 273	99.56 119	88.11 294	96.29 177	98.40 162

our_test_3		93.65 254	93.30 249	94.69 290	95.45 308	89.68 296	96.91 293	97.65 235	91.97 239	91.66 277	96.88 258	89.67 136	97.93 289	88.02 298	91.49 254	96.48 282

thres200		95.25 175	94.57 180	97.28 157	98.81 114	94.92 175	98.20 195	97.11 281	95.24 106	96.54 138	96.22 287	84.58 260	99.53 126	87.93 299	96.50 166	97.39 198

EG-PatchMatch MVS		91.13 290	90.12 291	94.17 303	94.73 320	89.00 306	98.13 207	97.81 228	89.22 303	85.32 318	96.46 277	67.71 340	98.42 248	87.89 300	93.82 222	95.08 314

CR-MVSNet		94.76 201	94.15 200	96.59 206	97.00 221	93.43 244	94.96 330	97.56 239	92.46 217	96.93 114	96.24 283	88.15 186	97.88 294	87.38 301	96.65 160	98.46 159

v11		91.85 283	90.68 285	95.36 271	96.34 262	94.74 204	98.80 104	97.43 262	89.60 297	85.09 323	93.03 325	88.53 178	96.75 320	87.37 302	79.96 329	94.58 327

Patchmtry		93.22 262	92.35 264	95.84 249	96.77 234	93.09 253	94.66 336	97.56 239	87.37 313	92.90 252	96.24 283	88.15 186	97.90 290	87.37 302	90.10 264	96.53 276

test0.0.03 1		94.08 243	93.51 243	95.80 251	95.53 305	92.89 255	97.38 269	95.97 319	95.11 110	92.51 263	96.66 269	87.71 201	96.94 311	87.03 304	93.67 223	97.57 193

TinyColmap		92.31 272	91.53 271	94.65 292	96.92 226	89.75 293	96.92 291	96.68 308	90.45 274	89.62 295	97.85 167	76.06 321	98.81 205	86.74 305	92.51 242	95.41 310

MIMVSNet		93.26 261	92.21 266	96.41 225	97.73 177	93.13 252	95.65 325	97.03 286	91.27 264	94.04 220	96.06 291	75.33 323	97.19 308	86.56 306	96.23 184	98.92 136

TransMVSNet (Re)		92.67 268	91.51 272	96.15 238	96.58 244	94.65 205	98.90 75	96.73 305	90.86 270	89.46 297	97.86 165	85.62 244	98.09 279	86.45 307	81.12 327	95.71 305

DSMNet-mixed		92.52 270	92.58 261	92.33 314	94.15 323	82.65 333	98.30 187	94.26 343	89.08 304	92.65 257	95.73 298	85.01 254	95.76 332	86.24 308	97.76 143	98.59 154

testgi		93.06 266	92.45 263	94.88 284	96.43 253	89.90 291	98.75 116	97.54 244	95.60 81	91.63 278	97.91 161	74.46 329	97.02 310	86.10 309	93.67 223	97.72 189

YYNet1		90.70 296	89.39 297	94.62 293	94.79 319	90.65 285	97.20 282	97.46 258	87.54 312	72.54 343	95.74 297	86.51 222	96.66 325	86.00 310	86.76 312	96.54 275

MDA-MVSNet_test_wron		90.71 295	89.38 298	94.68 291	94.83 318	90.78 282	97.19 283	97.46 258	87.60 311	72.41 344	95.72 300	86.51 222	96.71 324	85.92 311	86.80 311	96.56 273

UnsupCasMVSNet_bld		87.17 310	85.12 313	93.31 309	91.94 331	88.77 307	94.92 332	98.30 167	84.30 329	82.30 331	90.04 339	63.96 345	97.25 307	85.85 312	74.47 345	93.93 336

EPNet_dtu		95.21 178	94.95 156	95.99 243	96.17 279	90.45 288	98.16 204	97.27 276	96.77 44	93.14 247	98.33 131	90.34 129	98.42 248	85.57 313	98.81 103	99.09 120
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet5		91.81 284	90.92 278	94.49 295	97.21 210	92.09 262	98.00 221	97.55 243	89.31 302	90.86 286	95.61 303	74.48 328	95.32 334	85.57 313	89.70 267	96.07 297

tfpnnormal		93.66 252	92.70 259	96.55 214	96.94 225	95.94 123	98.97 68	99.19 15	91.04 268	91.38 279	97.34 205	84.94 255	98.61 216	85.45 315	89.02 280	95.11 313

Patchmatch-test		94.42 224	93.68 233	96.63 201	97.60 183	91.76 268	94.83 334	97.49 257	89.45 299	94.14 215	97.10 225	88.99 150	98.83 203	85.37 316	98.13 130	99.29 99

ppachtmachnet_test		93.22 262	92.63 260	94.97 281	95.45 308	90.84 279	96.88 299	97.88 226	90.60 271	92.08 273	97.26 210	88.08 190	97.86 296	85.12 317	90.33 262	96.22 292

PCF-MVS	93.45 11	94.68 210	93.43 246	98.42 85	98.62 129	96.77 88	95.48 326	98.20 183	84.63 328	93.34 240	98.32 132	88.55 177	99.81 53	84.80 318	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		89.97 300	88.35 306	94.83 287	95.21 313	91.34 273	97.64 255	97.51 248	88.36 308	71.17 345	96.13 290	79.22 307	96.63 326	83.65 319	86.27 313	96.52 277

MVS-HIRNet		89.46 303	88.40 305	92.64 312	97.58 185	82.15 334	94.16 340	93.05 349	75.73 343	90.90 285	82.52 345	79.42 306	98.33 263	83.53 320	98.68 105	97.43 195

new-patchmatchnet		88.50 308	87.45 309	91.67 317	90.31 336	85.89 326	97.16 285	97.33 272	89.47 298	83.63 330	92.77 331	76.38 319	95.06 336	82.70 321	77.29 340	94.06 334

PAPM		94.95 189	94.00 211	97.78 119	97.04 220	95.65 144	96.03 319	98.25 175	91.23 265	94.19 212	97.80 174	91.27 116	98.86 200	82.61 322	97.61 146	98.84 140

LCM-MVSNet		78.70 318	76.24 322	86.08 328	77.26 355	71.99 348	94.34 338	96.72 306	61.62 349	76.53 340	89.33 340	33.91 358	92.78 344	81.85 323	74.60 344	93.46 337

new_pmnet		90.06 299	89.00 302	93.22 311	94.18 322	88.32 316	96.42 315	96.89 301	86.19 317	85.67 317	93.62 316	77.18 318	97.10 309	81.61 324	89.29 275	94.23 330

pmmvs3		86.67 312	84.86 314	92.11 316	88.16 340	87.19 324	96.63 307	94.75 339	79.88 339	87.22 308	92.75 332	66.56 342	95.20 335	81.24 325	76.56 342	93.96 335

N_pmnet		87.12 311	87.77 308	85.17 331	95.46 307	61.92 354	97.37 271	70.66 361	85.83 322	88.73 303	96.04 292	85.33 251	97.76 297	80.02 326	90.48 261	95.84 301

TAPA-MVS	93.98 7	95.35 170	94.56 181	97.74 120	99.13 82	94.83 192	98.33 180	98.64 113	86.62 315	96.29 156	98.61 103	94.00 75	99.29 143	80.00 327	99.41 79	99.09 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepMVS_CX				86.78 327	97.09 219	72.30 347		95.17 336	75.92 342	84.34 329	95.19 304	70.58 336	95.35 333	79.98 328	89.04 279	92.68 339

Anonymous20231206		91.66 286	91.10 274	93.33 308	94.02 325	87.35 322	98.58 147	97.26 277	90.48 272	90.16 291	96.31 281	83.83 282	96.53 327	79.36 329	89.90 266	96.12 295

test20.03		90.89 294	90.38 288	92.43 313	93.48 326	88.14 317	98.33 180	97.56 239	93.40 188	87.96 305	96.71 268	80.69 299	94.13 338	79.15 330	86.17 314	95.01 317

PatchT		93.06 266	91.97 268	96.35 229	96.69 240	92.67 256	94.48 337	97.08 282	86.62 315	97.08 105	92.23 337	87.94 193	97.90 290	78.89 331	96.69 158	98.49 158

MIMVSNet1		89.67 302	88.28 307	93.82 304	92.81 330	91.08 278	98.01 219	97.45 260	87.95 309	87.90 306	95.87 296	67.63 341	94.56 337	78.73 332	88.18 293	95.83 302

test_0402		91.32 288	90.27 290	94.48 296	96.60 243	91.12 277	98.50 164	97.22 279	86.10 319	88.30 304	96.98 244	77.65 315	97.99 285	78.13 333	92.94 239	94.34 329

OpenMVS_ROB	86.42 20	89.00 304	87.43 310	93.69 305	93.08 328	89.42 299	97.91 230	96.89 301	78.58 340	85.86 314	94.69 310	69.48 337	98.29 270	77.13 334	93.29 235	93.36 338

testus		88.91 305	89.08 300	88.40 323	91.39 332	76.05 341	96.56 310	96.48 313	89.38 301	89.39 298	95.17 306	70.94 335	93.56 341	77.04 335	95.41 202	95.61 307

RPMNet		92.52 270	91.17 273	96.59 206	97.00 221	93.43 244	94.96 330	97.26 277	82.27 334	96.93 114	92.12 338	86.98 216	97.88 294	76.32 336	96.65 160	98.46 159

Anonymous20231211		83.69 315	81.50 317	90.26 319	89.23 339	80.10 337	97.97 223	97.06 285	72.79 345	82.05 333	92.57 333	50.28 349	96.32 330	76.15 337	75.38 343	94.37 328

test2356		88.68 307	88.61 303	88.87 322	89.90 338	78.23 338	95.11 328	96.66 311	88.66 307	89.06 300	94.33 315	73.14 333	92.56 345	75.56 338	95.11 204	95.81 303

LP		91.12 291	89.99 293	94.53 294	96.35 261	88.70 309	93.86 341	97.35 268	84.88 326	90.98 284	94.77 309	84.40 267	97.43 304	75.41 339	91.89 250	97.47 194

PMMVS2		77.95 320	75.44 323	85.46 329	82.54 347	74.95 346	94.23 339	93.08 348	72.80 344	74.68 341	87.38 341	36.36 356	91.56 347	73.95 340	63.94 347	89.87 341

no-one		74.41 322	70.76 324	85.35 330	79.88 350	76.83 339	94.68 335	94.22 344	80.33 338	63.81 348	79.73 349	35.45 357	93.36 342	71.78 341	36.99 354	85.86 347

test1235678		86.26 313	85.81 312	87.62 325	86.97 343	75.00 345	96.55 312	96.32 316	86.08 320	81.32 335	92.98 327	73.10 334	92.05 346	71.64 342	87.32 303	95.81 303

test12356		83.47 316	83.37 316	83.78 332	84.43 346	70.09 350	95.12 327	95.60 330	82.98 330	78.89 338	92.43 336	64.99 343	91.41 348	70.36 343	85.55 319	89.82 342

FPMVS		77.62 321	77.14 319	79.05 336	79.25 351	60.97 355	95.79 323	95.94 320	65.96 346	67.93 347	94.40 312	37.73 355	88.88 351	68.83 344	88.46 290	87.29 344

1111		84.94 314	84.30 315	86.86 326	87.59 341	75.10 343	96.63 307	96.43 314	82.53 332	80.75 336	92.91 329	68.94 338	93.79 339	68.24 345	84.66 320	91.70 340

.test1245		73.05 323	76.31 321	63.27 344	87.59 341	75.10 343	96.63 307	96.43 314	82.53 332	80.75 336	92.91 329	68.94 338	93.79 339	68.24 345	12.72 357	20.91 357

Gipuma		78.40 319	76.75 320	83.38 333	95.54 304	80.43 336	79.42 353	97.40 265	64.67 347	73.46 342	80.82 348	45.65 352	93.14 343	66.32 347	87.43 301	76.56 353
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testmv		78.74 317	77.35 318	82.89 334	78.16 354	69.30 351	95.87 321	94.65 340	81.11 336	70.98 346	87.11 343	46.31 350	90.42 349	65.28 348	76.72 341	88.95 343

wuykxyi23d		63.73 330	58.86 332	78.35 337	67.62 357	67.90 352	86.56 349	87.81 356	58.26 350	42.49 356	70.28 354	11.55 363	85.05 352	63.66 349	41.50 350	82.11 350

PNet_i23d		67.70 326	65.07 327	75.60 338	78.61 352	59.61 357	89.14 347	88.24 355	61.83 348	52.37 352	80.89 347	18.91 360	84.91 353	62.70 350	52.93 349	82.28 349

ANet_high		69.08 324	65.37 326	80.22 335	65.99 358	71.96 349	90.91 346	90.09 352	82.62 331	49.93 354	78.39 350	29.36 359	81.75 354	62.49 351	38.52 353	86.95 346

PMVS	61.03 23	65.95 327	63.57 329	73.09 341	57.90 359	51.22 360	85.05 351	93.93 347	54.45 351	44.32 355	83.57 344	13.22 361	89.15 350	58.68 352	81.00 328	78.91 352
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	62.14 22	63.28 331	59.38 331	74.99 339	74.33 356	65.47 353	85.55 350	80.50 360	52.02 353	51.10 353	75.00 353	10.91 365	80.50 355	51.60 353	53.40 348	78.99 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		64.94 328	64.25 328	67.02 342	82.28 348	59.36 358	91.83 345	85.63 357	52.69 352	60.22 350	77.28 351	41.06 354	80.12 356	46.15 354	41.14 351	61.57 355

EMVS		64.07 329	63.26 330	66.53 343	81.73 349	58.81 359	91.85 344	84.75 358	51.93 354	59.09 351	75.13 352	43.32 353	79.09 357	42.03 355	39.47 352	61.69 354

wuyk23d		30.17 333	30.18 335	30.16 346	78.61 352	43.29 361	66.79 354	14.21 362	17.31 356	14.82 359	11.93 360	11.55 363	41.43 359	37.08 356	19.30 356	5.76 359

test123		20.95 336	23.72 337	12.64 347	13.54 362	8.19 362	96.55 312	6.13 364	7.48 358	16.74 358	37.98 357	12.97 362	6.05 360	16.69 357	5.43 359	23.68 356

testmvs		21.48 335	24.95 336	11.09 348	14.89 361	6.47 363	96.56 310	9.87 363	7.55 357	17.93 357	39.02 356	9.43 366	5.90 361	16.56 358	12.72 357	20.91 357

cdsmvs_eth3d_5k		23.98 334	31.98 334	0.00 349	0.00 363	0.00 364	0.00 355	98.59 116	0.00 359	0.00 360	98.61 103	90.60 126	0.00 362	0.00 359	0.00 360	0.00 360

pcd_1.5k_mvsjas		7.88 338	10.50 339	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	94.51 63	0.00 362	0.00 359	0.00 360	0.00 360

pcd1.5k->3k		39.42 332	41.78 333	32.35 345	96.17 279	0.00 364	0.00 355	98.54 126	0.00 359	0.00 360	0.00 361	87.78 200	0.00 362	0.00 359	93.56 227	97.06 210

sosnet-low-res		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

sosnet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uncertanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

Regformer		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

ab-mvs-re		8.20 337	10.94 338	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	98.43 118	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

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												89.45 139				99.20 107

sam_mvs												88.99 150

MTGPA								98.74 80

test_post											31.83 358	88.83 162	98.91 192

patchmatchnet-post											95.10 307	89.42 140	98.89 196

MTMP								94.14 345

TEST9					99.31 50	98.50 15	97.92 227	98.73 85	92.63 212	97.74 84	98.68 97	96.20 15	99.80 60

test_8					99.29 58	98.44 17	97.89 235	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	97.86 238

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

新几何2							97.64 255

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							97.67 253

test222					99.23 73	97.17 75	97.40 267	98.66 108	88.68 306	98.05 63	98.96 72	94.14 72			99.53 68	99.61 59

segment_acmp												96.85 6

testdata1							97.32 277		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	94.63 207

plane_prior6					97.35 202	94.61 210						87.09 213

plane_prior4											98.28 134

plane_prior3						94.61 210			97.02 39	95.34 167

plane_prior2							98.80 104		97.28 21

plane_prior1					97.37 201

plane_prior						94.60 212	98.44 169		96.74 46						94.22 209

n2								0.00 365

nn								0.00 365

door-mid								94.37 342

test11								98.66 108

door								94.64 341

HQP5-MVS						94.25 225

HQP-NCC					97.20 211		98.05 215		96.43 54	94.45 188

ACMP_Plane					97.20 211		98.05 215		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												86.75 219

NP-MVS					97.28 205	94.51 215					97.73 177

ACMMP++_ref															92.97 238

ACMMP++															93.61 226

Test By Simon												94.64 60

High-resolution multi-view benchmark