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.

DeepPCF-MVS	93.56 1	96.55 33	97.84 5	92.68 193	98.71 72	78.11 303	99.70 10	97.71 78	98.18 1	97.36 37	99.76 1	90.37 38	99.94 23	99.27 3	99.54 42	99.99 1

MCST-MVS		98.18 2	97.95 4	98.86 1	99.85 3	96.60 5	99.70 10	97.98 52	97.18 2	95.96 62	99.33 9	92.62 12	100.00 1	98.99 6	99.93 1	99.98 2

CNVR-MVS		98.46 1	98.38 1	98.72 3	99.80 4	96.19 9	99.80 7	97.99 51	97.05 3	99.41 1	99.59 2	92.89 11	100.00 1	98.99 6	99.90 4	99.96 4

EPNet		96.82 26	96.68 26	97.25 44	98.65 73	93.10 63	99.48 26	98.76 18	96.54 4	97.84 30	98.22 94	87.49 72	99.66 66	95.35 65	97.78 92	99.00 94
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
NCCC		98.12 3	98.11 3	98.13 15	99.76 6	94.46 39	99.81 5	97.88 57	96.54 4	98.84 7	99.46 6	92.55 13	99.98 9	98.25 23	99.93 1	99.94 6

DeepC-MVS_fast	93.52 2	97.16 15	96.84 20	98.13 15	99.61 17	94.45 40	98.85 98	97.64 88	96.51 6	95.88 63	99.39 8	87.35 79	99.99 4	96.61 42	99.69 28	99.96 4
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DELS-MVS		97.12 16	96.60 27	98.68 5	98.03 86	96.57 6	99.84 3	97.84 61	96.36 7	95.20 76	98.24 93	88.17 62	99.83 48	96.11 53	99.60 38	99.64 53
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
CANet		97.00 20	96.49 28	98.55 6	98.86 68	96.10 10	99.83 4	97.52 109	95.90 8	97.21 38	98.90 58	82.66 143	99.93 25	98.71 9	98.80 74	99.63 55

PS-MVSNAJ		96.87 25	96.40 30	98.29 11	97.35 108	97.29 1	99.03 77	97.11 148	95.83 9	98.97 4	99.14 29	82.48 146	99.60 77	98.60 11	99.08 60	98.00 154

MVS_0304		96.12 46	95.26 56	98.69 4	98.44 78	96.54 7	99.70 10	96.89 165	95.76 10	97.53 33	99.12 32	72.42 231	99.93 25	98.75 8	98.69 77	99.61 58

HPM-MVS++		97.72 6	97.59 7	98.14 14	99.53 33	94.76 30	99.19 53	97.75 73	95.66 11	98.21 16	99.29 10	91.10 19	99.99 4	97.68 29	99.87 5	99.68 48

CANet_DTU		94.31 83	93.35 88	97.20 46	97.03 119	94.71 32	98.62 124	95.54 243	95.61 12	97.21 38	98.47 88	71.88 237	99.84 46	88.38 144	97.46 98	97.04 178

xiu_mvs_v2_base		96.66 29	96.17 38	98.11 17	97.11 116	96.96 2	99.01 80	97.04 156	95.51 13	98.86 6	99.11 35	82.19 152	99.36 100	98.59 13	98.14 86	98.00 154

HSP-MVS		97.73 5	98.15 2	96.44 92	99.54 27	90.14 128	99.41 38	97.47 117	95.46 14	98.60 9	99.19 19	95.71 4	99.49 87	98.15 24	99.85 9	99.69 47

TSAR-MVS + GP.		96.95 22	96.91 18	97.07 48	98.88 66	91.62 87	99.58 18	96.54 179	95.09 15	96.84 50	98.63 77	91.16 17	99.77 58	99.04 5	96.42 109	99.81 23

MSLP-MVS++		97.50 9	97.45 10	97.63 28	99.65 13	93.21 59	99.70 10	98.13 45	94.61 16	97.78 31	99.46 6	89.85 41	99.81 53	97.97 25	99.91 3	99.88 15

TSAR-MVS + MP.		97.44 10	97.46 9	97.39 40	99.12 53	93.49 57	98.52 136	97.50 114	94.46 17	98.99 3	98.64 76	91.58 16	99.08 115	98.49 17	99.83 12	99.60 59

MG-MVS		97.24 12	96.83 21	98.47 9	99.79 5	95.71 12	99.07 72	99.06 15	94.45 18	96.42 57	98.70 73	88.81 52	99.74 61	95.35 65	99.86 8	99.97 3

PAPM		96.35 39	95.94 43	97.58 30	94.10 198	95.25 16	98.93 86	98.17 41	94.26 19	93.94 94	98.72 71	89.68 44	97.88 158	96.36 48	99.29 55	99.62 57

CLD-MVS		91.06 155	90.71 151	92.10 200	94.05 201	86.10 221	99.55 22	96.29 194	94.16 20	84.70 192	97.17 131	69.62 252	97.82 162	94.74 76	86.08 209	92.39 214
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
SteuartSystems-ACMMP		97.25 11	97.34 12	97.01 51	97.38 107	91.46 91	99.75 8	97.66 83	94.14 21	98.13 17	99.26 11	92.16 14	99.66 66	97.91 27	99.64 31	99.90 9
Skip Steuart: Steuart Systems R&D Blog.
HQP-NCC					93.95 202		99.16 58		93.92 22	87.57 172

ACMP_Plane					93.95 202		99.16 58		93.92 22	87.57 172

HQP-MVS		91.50 147	91.23 133	92.29 197	93.95 202	86.39 210	99.16 58	96.37 186	93.92 22	87.57 172	96.67 150	73.34 221	97.77 166	93.82 88	86.29 204	92.72 209

DeepC-MVS	91.02 4	94.56 79	93.92 82	96.46 90	97.16 114	90.76 117	98.39 156	97.11 148	93.92 22	88.66 161	98.33 91	78.14 177	99.85 45	95.02 70	98.57 81	98.78 117
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_111021_HR		96.69 28	96.69 25	96.72 76	98.58 76	91.00 111	99.14 66	99.45 1	93.86 26	95.15 77	98.73 69	88.48 57	99.76 59	97.23 32	99.56 41	99.40 70

lupinMVS		96.32 41	95.94 43	97.44 36	95.05 182	94.87 22	99.86 2	96.50 180	93.82 27	98.04 23	98.77 65	85.52 103	98.09 147	96.98 38	98.97 65	99.37 71

plane_prior						86.07 223	99.14 66		93.81 28						86.26 206

SD-MVS		97.51 8	97.40 11	97.81 24	99.01 59	93.79 51	99.33 49	97.38 129	93.73 29	98.83 8	99.02 42	90.87 30	99.88 35	98.69 10	99.74 21	99.77 35

plane_prior3						85.91 227			93.65 30	86.99 178

PVSNet_Blended		95.94 50	95.66 50	96.75 72	98.77 70	91.61 88	99.88 1	98.04 48	93.64 31	94.21 90	97.76 104	83.50 124	99.87 38	97.41 30	97.75 93	98.79 114

APDe-MVS		97.53 7	97.47 8	97.70 26	99.58 19	93.63 52	99.56 21	97.52 109	93.59 32	98.01 25	99.12 32	90.80 32	99.55 79	99.26 4	99.79 17	99.93 7

jason		95.40 62	94.86 62	97.03 50	92.91 227	94.23 45	99.70 10	96.30 191	93.56 33	96.73 54	98.52 82	81.46 157	97.91 155	96.08 54	98.47 83	98.96 99
jason: jason.
MVS_111021_LR		95.78 55	95.94 43	95.28 131	98.19 83	87.69 173	98.80 103	99.26 13	93.39 34	95.04 79	98.69 74	84.09 120	99.76 59	96.96 39	99.06 61	98.38 139

HQP_MVS		91.26 151	90.95 143	92.16 199	93.84 209	86.07 223	99.02 78	96.30 191	93.38 35	86.99 178	96.52 156	72.92 226	97.75 171	93.46 93	86.17 207	92.67 211

plane_prior2							99.02 78		93.38 35

xiu_mvs_v1_base_debu		94.73 71	93.98 76	96.99 54	95.19 172	95.24 17	98.62 124	96.50 180	92.99 37	97.52 34	98.83 62	72.37 232	99.15 109	97.03 34	96.74 104	96.58 192

xiu_mvs_v1_base		94.73 71	93.98 76	96.99 54	95.19 172	95.24 17	98.62 124	96.50 180	92.99 37	97.52 34	98.83 62	72.37 232	99.15 109	97.03 34	96.74 104	96.58 192

xiu_mvs_v1_base_debi		94.73 71	93.98 76	96.99 54	95.19 172	95.24 17	98.62 124	96.50 180	92.99 37	97.52 34	98.83 62	72.37 232	99.15 109	97.03 34	96.74 104	96.58 192

EPNet_dtu		92.28 130	92.15 113	92.70 192	97.29 110	84.84 245	98.64 122	97.82 63	92.91 40	93.02 105	97.02 138	85.48 108	95.70 271	72.25 297	94.89 131	97.55 167
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Regformer-1		96.97 21	96.80 22	97.47 34	99.46 37	93.11 62	98.89 95	97.94 53	92.89 41	96.90 43	99.02 42	89.78 42	99.53 81	97.06 33	99.26 57	99.75 36

OPM-MVS		89.76 177	89.15 168	91.57 214	90.53 256	85.58 236	98.11 183	95.93 215	92.88 42	86.05 184	96.47 159	67.06 273	97.87 159	89.29 138	86.08 209	91.26 249

Regformer-2		96.94 24	96.78 23	97.42 37	99.46 37	92.97 67	98.89 95	97.93 54	92.86 43	96.88 44	99.02 42	89.74 43	99.53 81	97.03 34	99.26 57	99.75 36

test_part3							99.43 33		92.81 44		99.48 4		99.97 14	99.52 1

ESAPD		97.97 4	97.82 6	98.43 10	99.54 27	95.42 14	99.43 33	97.69 79	92.81 44	98.13 17	99.48 4	93.96 6	99.97 14	99.52 1	99.83 12	99.90 9

zzz-MVS		96.21 45	95.96 42	96.96 59	99.29 45	91.19 102	98.69 113	97.45 119	92.58 46	94.39 86	99.24 14	86.43 95	99.99 4	96.22 49	99.40 51	99.71 43

MTAPA		96.09 47	95.80 48	96.96 59	99.29 45	91.19 102	97.23 215	97.45 119	92.58 46	94.39 86	99.24 14	86.43 95	99.99 4	96.22 49	99.40 51	99.71 43

CHOSEN 280x420		96.80 27	96.85 19	96.66 80	97.85 88	94.42 42	94.76 282	98.36 26	92.50 48	95.62 70	97.52 111	97.92 1	97.38 195	98.31 22	98.80 74	98.20 149

Regformer-3		96.50 34	96.36 32	96.91 61	99.34 40	91.72 85	98.71 109	97.90 56	92.48 49	96.00 59	98.95 53	88.60 54	99.52 84	96.44 46	98.83 71	99.49 66

Regformer-4		96.45 37	96.33 34	96.81 69	99.34 40	91.44 92	98.71 109	97.88 57	92.43 50	95.97 61	98.95 53	88.42 58	99.51 85	96.40 47	98.83 71	99.49 66

testdata1							97.89 193		92.43 50

PAPR		96.35 39	95.82 46	97.94 20	99.63 14	94.19 46	99.42 37	97.55 105	92.43 50	93.82 98	99.12 32	87.30 80	99.91 29	94.02 82	99.06 61	99.74 39

HY-MVS	88.56 7	95.29 63	94.23 70	98.48 8	97.72 91	96.41 8	94.03 290	98.74 19	92.42 53	95.65 69	94.76 182	86.52 92	99.49 87	95.29 67	92.97 143	99.53 62

XVS		96.47 36	96.37 31	96.77 70	99.62 15	90.66 121	99.43 33	97.58 98	92.41 54	96.86 47	98.96 51	87.37 75	99.87 38	95.65 58	99.43 48	99.78 30

X-MVStestdata		90.69 164	88.66 177	96.77 70	99.62 15	90.66 121	99.43 33	97.58 98	92.41 54	96.86 47	29.59 357	87.37 75	99.87 38	95.65 58	99.43 48	99.78 30

UGNet		91.91 143	90.85 145	95.10 135	97.06 118	88.69 158	98.01 190	98.24 30	92.41 54	92.39 110	93.61 202	60.52 302	99.68 63	88.14 146	97.25 100	96.92 184
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
WTY-MVS		95.97 49	95.11 59	98.54 7	97.62 94	96.65 4	99.44 31	98.74 19	92.25 57	95.21 75	98.46 90	86.56 91	99.46 94	95.00 71	92.69 147	99.50 65

OMC-MVS		93.90 88	93.62 86	94.73 147	98.63 74	87.00 192	98.04 189	96.56 177	92.19 58	92.46 108	98.73 69	79.49 166	99.14 112	92.16 109	94.34 135	98.03 153

CHOSEN 1792x2688		94.35 82	93.82 84	95.95 110	97.40 106	88.74 157	98.41 152	98.27 28	92.18 59	91.43 121	96.40 160	78.88 169	99.81 53	93.59 91	97.81 89	99.30 77

PVSNet_Blended_VisFu		94.67 75	94.11 72	96.34 97	97.14 115	91.10 107	99.32 50	97.43 124	92.10 60	91.53 119	96.38 163	83.29 130	99.68 63	93.42 95	96.37 110	98.25 146

Effi-MVS+-dtu		89.97 175	90.68 152	87.81 287	95.15 176	71.98 321	97.87 196	95.40 254	91.92 61	87.57 172	91.44 234	74.27 208	96.84 211	89.45 131	93.10 142	94.60 202

mvs-test1		91.57 146	92.20 111	89.70 251	95.15 176	74.34 312	99.51 25	95.40 254	91.92 61	91.02 127	97.25 124	74.27 208	98.08 150	89.45 131	95.83 122	96.67 185

EI-MVSNet-Vis-set		95.76 57	95.63 53	96.17 102	99.14 52	90.33 124	98.49 142	97.82 63	91.92 61	94.75 82	98.88 60	87.06 83	99.48 92	95.40 64	97.17 102	98.70 122

canonicalmvs		95.02 66	93.96 79	98.20 12	97.53 101	95.92 11	98.71 109	96.19 200	91.78 64	95.86 65	98.49 86	79.53 165	99.03 116	96.12 52	91.42 167	99.66 51

EI-MVSNet-UG-set		95.43 59	95.29 55	95.86 113	99.07 57	89.87 137	98.43 149	97.80 68	91.78 64	94.11 92	98.77 65	86.25 98	99.48 92	94.95 73	96.45 108	98.22 147

Vis-MVSNet		92.64 125	91.85 120	95.03 141	95.12 178	88.23 164	98.48 143	96.81 166	91.61 66	92.16 113	97.22 127	71.58 241	98.00 154	85.85 169	97.81 89	98.88 107
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator	87.35 11	93.17 113	91.77 123	97.37 42	95.41 167	93.07 64	98.82 101	97.85 60	91.53 67	82.56 220	97.58 110	71.97 236	99.82 51	91.01 116	99.23 59	99.22 85

alignmvs		95.77 56	95.00 61	98.06 18	97.35 108	95.68 13	99.71 9	97.50 114	91.50 68	96.16 58	98.61 78	86.28 97	99.00 117	96.19 51	91.74 161	99.51 64

PVSNet_BlendedMVS		93.36 104	93.20 91	93.84 172	98.77 70	91.61 88	99.47 27	98.04 48	91.44 69	94.21 90	92.63 221	83.50 124	99.87 38	97.41 30	83.37 227	90.05 285

test_prior3		97.07 19	97.09 13	97.01 51	99.58 19	91.77 82	99.57 19	97.57 102	91.43 70	98.12 20	98.97 48	90.43 36	99.49 87	98.33 19	99.81 15	99.79 26

test_prior2							99.57 19		91.43 70	98.12 20	98.97 48	90.43 36		98.33 19	99.81 15

PVSNet	87.13 12	93.69 93	92.83 98	96.28 98	97.99 87	90.22 127	99.38 40	98.93 16	91.42 72	93.66 99	97.68 107	71.29 243	99.64 72	87.94 148	97.20 101	98.98 97

3Dnovator+	87.72 8	93.43 101	91.84 121	98.17 13	95.73 159	95.08 20	98.92 88	97.04 156	91.42 72	81.48 238	97.60 109	74.60 198	99.79 56	90.84 119	98.97 65	99.64 53

PatchFormer-LS_test		94.08 85	93.60 87	95.53 125	96.92 121	89.57 144	96.51 240	97.34 133	91.29 74	92.22 112	97.18 129	91.66 15	98.02 152	87.05 155	92.21 154	99.00 94

PMMVS		93.62 98	93.90 83	92.79 189	96.79 132	81.40 276	98.85 98	96.81 166	91.25 75	96.82 52	98.15 98	77.02 183	98.13 146	93.15 99	96.30 113	98.83 111

IB-MVS	89.43 6	92.12 137	90.83 148	95.98 108	95.40 168	90.78 116	99.81 5	98.06 46	91.23 76	85.63 187	93.66 201	90.63 33	98.78 121	91.22 113	71.85 299	98.36 142
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		94.36 81	93.95 80	95.62 118	96.99 120	89.47 146	96.62 237	97.38 129	90.96 77	93.07 104	97.27 123	93.73 8	98.09 147	85.86 168	93.65 139	99.29 78

#test#		96.48 35	96.34 33	96.90 62	99.69 8	90.96 112	99.53 24	97.81 66	90.94 78	96.88 44	99.05 39	87.57 69	99.96 18	95.87 57	99.72 23	99.78 30

UA-Net		93.30 107	92.62 102	95.34 129	96.27 144	88.53 162	95.88 266	96.97 162	90.90 79	95.37 73	97.07 136	82.38 149	99.10 114	83.91 187	94.86 132	98.38 139

ACMMP_Plus		96.59 31	96.18 36	97.81 24	98.82 69	93.55 54	98.88 97	97.59 96	90.66 80	97.98 26	99.14 29	86.59 90	100.00 1	96.47 45	99.46 45	99.89 14

mPP-MVS		95.90 51	95.75 49	96.38 95	99.58 19	89.41 148	99.26 51	97.41 126	90.66 80	94.82 81	98.95 53	86.15 99	99.98 9	95.24 68	99.64 31	99.74 39

PAPM_NR		95.43 59	95.05 60	96.57 87	99.42 39	90.14 128	98.58 131	97.51 111	90.65 82	92.44 109	98.90 58	87.77 68	99.90 31	90.88 118	99.32 54	99.68 48

MP-MVS		96.00 48	95.82 46	96.54 88	99.47 36	90.13 130	99.36 44	97.41 126	90.64 83	95.49 71	98.95 53	85.51 105	99.98 9	96.00 56	99.59 40	99.52 63

region2R		96.30 42	96.17 38	96.70 77	99.70 7	90.31 125	99.46 30	97.66 83	90.55 84	97.07 41	99.07 36	86.85 87	99.97 14	95.43 63	99.74 21	99.81 23

HFP-MVS		96.42 38	96.26 35	96.90 62	99.69 8	90.96 112	99.47 27	97.81 66	90.54 85	96.88 44	99.05 39	87.57 69	99.96 18	95.65 58	99.72 23	99.78 30

ACMMPR		96.28 43	96.14 41	96.73 74	99.68 10	90.47 123	99.47 27	97.80 68	90.54 85	96.83 51	99.03 41	86.51 93	99.95 21	95.65 58	99.72 23	99.75 36

EPMVS		92.59 127	91.59 127	95.59 120	97.22 112	90.03 135	91.78 310	98.04 48	90.42 87	91.66 115	90.65 257	86.49 94	97.46 186	81.78 209	96.31 112	99.28 80

agg_prior1		97.12 16	97.03 15	97.38 41	99.54 27	92.66 72	99.35 46	97.64 88	90.38 88	97.98 26	99.17 23	90.84 31	99.61 75	98.57 16	99.78 19	99.87 19

ACMMP		94.67 75	94.30 68	95.79 114	99.25 48	88.13 166	98.41 152	98.67 23	90.38 88	91.43 121	98.72 71	82.22 151	99.95 21	93.83 87	95.76 123	99.29 78
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
VNet		95.08 65	94.26 69	97.55 33	98.07 85	93.88 50	98.68 116	98.73 21	90.33 90	97.16 40	97.43 115	79.19 168	99.53 81	96.91 40	91.85 159	99.24 83

test-LLR		93.11 114	92.68 100	94.40 155	94.94 186	87.27 189	99.15 63	97.25 135	90.21 91	91.57 116	94.04 187	84.89 113	97.58 180	85.94 165	96.13 115	98.36 142

test0.0.03 1		88.96 187	88.61 178	90.03 244	91.09 250	84.43 249	98.97 84	97.02 159	90.21 91	80.29 245	96.31 164	84.89 113	91.93 326	72.98 293	85.70 212	93.73 204

train_agg		97.20 14	97.08 14	97.57 32	99.57 23	93.17 60	99.38 40	97.66 83	90.18 93	98.39 12	99.18 21	90.94 27	99.66 66	98.58 14	99.85 9	99.88 15

test_8					99.55 26	93.07 64	99.37 43	97.64 88	90.18 93	98.36 14	99.19 19	90.94 27	99.64 72

1314		93.44 100	91.98 119	97.84 22	95.24 169	94.38 43	96.22 252	97.92 55	90.18 93	82.28 225	97.71 106	77.63 180	99.80 55	91.94 111	98.67 79	99.34 74

CVMVSNet		90.30 166	90.91 144	88.46 274	94.32 195	73.58 316	97.61 204	97.59 96	90.16 96	88.43 162	97.10 134	76.83 184	92.86 306	82.64 198	93.54 140	98.93 104

MVSTER		92.71 122	92.32 106	93.86 171	97.29 110	92.95 68	99.01 80	96.59 173	90.09 97	85.51 188	94.00 191	94.61 5	96.56 219	90.77 121	83.03 230	92.08 228

APD-MVS		96.95 22	96.72 24	97.63 28	99.51 34	93.58 53	99.16 58	97.44 122	90.08 98	98.59 10	99.07 36	89.06 48	99.42 95	97.92 26	99.66 29	99.88 15
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		96.22 44	96.15 40	96.42 93	99.67 11	89.62 143	99.70 10	97.61 94	90.07 99	96.00 59	99.16 25	87.43 73	99.92 27	96.03 55	99.72 23	99.70 45

Patchmatch-test1		90.10 171	88.61 178	94.57 151	94.95 185	88.83 153	96.26 248	97.21 140	90.06 100	90.03 144	90.68 253	66.61 276	95.83 268	77.31 244	94.36 134	99.05 92

PVSNet_0	83.28 16	87.31 209	85.16 227	93.74 175	94.78 189	84.59 248	98.91 89	98.69 22	89.81 101	78.59 264	93.23 211	61.95 297	99.34 103	94.75 75	55.72 338	97.30 171

GA-MVS		90.10 171	88.69 176	94.33 157	92.44 231	87.97 170	99.08 71	96.26 196	89.65 102	86.92 180	93.11 214	68.09 263	96.96 207	82.54 199	90.15 182	98.05 151

agg_prior3		97.09 18	96.97 17	97.45 35	99.56 25	92.79 71	99.36 44	97.67 82	89.59 103	98.36 14	99.16 25	90.57 34	99.68 63	98.58 14	99.85 9	99.88 15

MDTV_nov1_ep13			90.47 156		96.14 150	88.55 160	91.34 313	97.51 111	89.58 104	92.24 111	90.50 265	86.99 86	97.61 179	77.64 243	92.34 150

TEST9					99.57 23	93.17 60	99.38 40	97.66 83	89.57 105	98.39 12	99.18 21	90.88 29	99.66 66

PatchmatchNet		92.05 142	91.04 135	95.06 139	96.17 148	89.04 151	91.26 314	97.26 134	89.56 106	90.64 133	90.56 263	88.35 60	97.11 202	79.53 226	96.07 119	99.03 93
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SMA-MVS		97.21 13	96.98 16	97.91 21	99.30 44	93.93 48	99.16 58	97.58 98	89.53 107	99.35 2	99.52 3	90.24 39	99.99 4	98.32 21	99.77 20	99.82 22

sss		94.85 69	93.94 81	97.58 30	96.43 140	94.09 47	98.93 86	99.16 14	89.50 108	95.27 74	97.85 100	81.50 156	99.65 70	92.79 104	94.02 137	98.99 96

ACMP	87.39 10	88.71 196	88.24 186	90.12 241	93.91 207	81.06 283	98.50 140	95.67 233	89.43 109	80.37 244	95.55 171	65.67 281	97.83 161	90.55 122	84.51 218	91.47 242
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_normal		89.37 183	87.18 199	95.93 111	88.94 293	90.83 115	98.24 171	96.62 171	89.31 110	70.38 303	90.20 275	63.50 291	98.37 139	92.06 110	95.41 128	98.59 128

abl_6		94.63 77	94.48 65	95.09 136	98.61 75	86.96 193	98.06 188	96.97 162	89.31 110	95.86 65	98.56 80	79.82 163	99.64 72	94.53 80	98.65 80	98.66 124

DI_MVS_plusplus_test		89.41 182	87.24 197	95.92 112	89.06 290	90.75 119	98.18 178	96.63 170	89.29 112	70.54 301	90.31 268	63.50 291	98.40 138	92.25 108	95.44 127	98.60 125

thres200		93.69 93	92.59 103	96.97 58	97.76 89	94.74 31	99.35 46	99.36 2	89.23 113	91.21 126	96.97 141	83.42 127	98.77 122	85.08 172	90.96 170	97.39 169

PGM-MVS		95.85 52	95.65 51	96.45 91	99.50 35	89.77 140	98.22 173	98.90 17	89.19 114	96.74 53	98.95 53	85.91 101	99.92 27	93.94 83	99.46 45	99.66 51

Test4		85.71 243	82.59 259	95.07 138	84.45 321	89.84 139	97.20 217	95.73 228	89.19 114	64.59 326	87.58 297	40.59 340	96.77 214	88.95 142	95.01 130	98.60 125

TESTMET0.1,1		93.82 90	93.26 90	95.49 126	95.21 171	90.25 126	99.15 63	97.54 108	89.18 116	91.79 114	94.87 180	89.13 47	97.63 177	86.21 162	96.29 114	98.60 125

UniMVSNet (Re)		89.50 181	88.32 185	93.03 184	92.21 234	90.96 112	98.90 94	98.39 25	89.13 117	83.22 203	92.03 224	81.69 154	96.34 244	86.79 160	72.53 290	91.81 233

FIs		90.70 163	89.87 160	93.18 181	92.29 232	91.12 105	98.17 181	98.25 29	89.11 118	83.44 202	94.82 181	82.26 150	96.17 255	87.76 149	82.76 232	92.25 219

tpmrst		92.78 117	92.16 112	94.65 149	96.27 144	87.45 180	91.83 309	97.10 151	89.10 119	94.68 84	90.69 251	88.22 61	97.73 173	89.78 128	91.80 160	98.77 118

CDPH-MVS		96.56 32	96.18 36	97.70 26	99.59 18	93.92 49	99.13 69	97.44 122	89.02 120	97.90 29	99.22 16	88.90 51	99.49 87	94.63 78	99.79 17	99.68 48

原ACMM1				96.18 100	99.03 58	90.08 131		97.63 92	88.98 121	97.00 42	98.97 48	88.14 64	99.71 62	88.23 145	99.62 35	98.76 119

XVG-OURS		90.83 160	90.49 155	91.86 203	95.23 170	81.25 280	95.79 271	95.92 216	88.96 122	90.02 145	98.03 99	71.60 240	99.35 102	91.06 115	87.78 200	94.98 200

MP-MVS-pluss		95.80 54	95.30 54	97.29 43	98.95 63	92.66 72	98.59 130	97.14 145	88.95 123	93.12 102	99.25 12	85.62 102	99.94 23	96.56 44	99.48 44	99.28 80
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test-mter		93.27 109	92.89 97	94.40 155	94.94 186	87.27 189	99.15 63	97.25 135	88.95 123	91.57 116	94.04 187	88.03 66	97.58 180	85.94 165	96.13 115	98.36 142

APD-MVS_3200maxsize		95.64 58	95.65 51	95.62 118	99.24 49	87.80 172	98.42 150	97.22 139	88.93 125	96.64 56	98.98 47	85.49 106	99.36 100	96.68 41	99.27 56	99.70 45

CR-MVSNet		88.83 191	87.38 194	93.16 182	93.47 217	86.24 215	84.97 332	94.20 285	88.92 126	90.76 131	86.88 305	84.43 117	94.82 291	70.64 302	92.17 156	98.41 135

DU-MVS		88.83 191	87.51 192	92.79 189	91.46 246	90.07 132	98.71 109	97.62 93	88.87 127	83.21 204	93.68 199	74.63 196	95.93 265	86.95 157	72.47 291	92.36 215

FC-MVSNet-test		90.22 168	89.40 164	92.67 194	91.78 242	89.86 138	97.89 193	98.22 31	88.81 128	82.96 211	94.66 183	81.90 153	95.96 263	85.89 167	82.52 235	92.20 224

USDC		84.74 248	82.93 250	90.16 240	91.73 243	83.54 257	95.00 280	93.30 296	88.77 129	73.19 291	93.30 209	53.62 321	97.65 176	75.88 259	81.54 239	89.30 295

testgi		82.29 268	81.00 272	86.17 298	87.24 313	74.84 311	97.39 207	91.62 324	88.63 130	75.85 281	95.42 175	46.07 333	91.55 328	66.87 311	79.94 244	92.12 226

VPNet		88.30 200	86.57 202	93.49 176	91.95 238	91.35 99	98.18 178	97.20 141	88.61 131	84.52 195	94.89 179	62.21 296	96.76 215	89.34 135	72.26 295	92.36 215

IS-MVSNet		93.00 115	92.51 104	94.49 152	96.14 150	87.36 186	98.31 161	95.70 231	88.58 132	90.17 142	97.50 112	83.02 139	97.22 198	87.06 154	96.07 119	98.90 106

PS-MVSNAJss		89.54 180	89.05 169	91.00 224	88.77 294	84.36 250	97.39 207	95.97 209	88.47 133	81.88 234	93.80 197	82.48 146	96.50 226	89.34 135	83.34 228	92.15 225

jajsoiax		87.35 208	86.51 204	89.87 245	87.75 308	81.74 273	97.03 223	95.98 207	88.47 133	80.15 247	93.80 197	61.47 298	96.36 238	89.44 133	84.47 220	91.50 241

Fast-Effi-MVS+-dtu		88.84 190	88.59 181	89.58 254	93.44 220	78.18 301	98.65 120	94.62 275	88.46 135	84.12 198	95.37 176	68.91 257	96.52 225	82.06 203	91.70 163	94.06 203

tfpn200view9		93.43 101	92.27 109	96.90 62	97.68 92	94.84 24	99.18 55	99.36 2	88.45 136	90.79 129	96.90 143	83.31 128	98.75 124	84.11 183	90.69 172	97.12 173

thres400		93.39 103	92.27 109	96.73 74	97.68 92	94.84 24	99.18 55	99.36 2	88.45 136	90.79 129	96.90 143	83.31 128	98.75 124	84.11 183	90.69 172	96.61 186

LCM-MVSNet-Re		88.59 197	88.61 178	88.51 273	95.53 164	72.68 319	96.85 227	88.43 342	88.45 136	73.14 292	90.63 258	75.82 187	94.38 296	92.95 100	95.71 124	98.48 133

PLC	91.07 3	94.23 84	94.01 75	94.87 143	99.17 51	87.49 178	99.25 52	96.55 178	88.43 139	91.26 124	98.21 96	85.92 100	99.86 43	89.77 129	97.57 94	97.24 172
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
XVG-OURS-SEG-HR		90.95 158	90.66 153	91.83 204	95.18 175	81.14 282	95.92 263	95.92 216	88.40 140	90.33 141	97.85 100	70.66 246	99.38 98	92.83 103	88.83 196	94.98 200

UniMVSNet_NR-MVSNet		89.60 179	88.55 182	92.75 191	92.17 235	90.07 132	98.74 108	98.15 43	88.37 141	83.21 204	93.98 192	82.86 141	95.93 265	86.95 157	72.47 291	92.25 219

MAR-MVS		94.43 80	94.09 73	95.45 127	99.10 55	87.47 179	98.39 156	97.79 70	88.37 141	94.02 93	99.17 23	78.64 175	99.91 29	92.48 105	98.85 70	98.96 99
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)		93.26 110	93.00 96	94.06 165	96.14 150	86.71 202	98.68 116	96.70 169	88.30 143	89.71 151	97.64 108	85.43 109	96.39 236	88.06 147	96.32 111	99.08 91

1112_ss		92.71 122	91.55 128	96.20 99	95.56 163	91.12 105	98.48 143	94.69 273	88.29 144	86.89 181	98.50 84	87.02 84	98.66 131	84.75 175	89.77 188	98.81 112

Test_1112_low_res		92.27 131	90.97 142	96.18 100	95.53 164	91.10 107	98.47 145	94.66 274	88.28 145	86.83 182	93.50 206	87.00 85	98.65 132	84.69 176	89.74 189	98.80 113

gm-plane-assit					94.69 191	88.14 165			88.22 146		97.20 128		98.29 141	90.79 120

mvs_tets		87.09 217	86.22 207	89.71 250	87.87 304	81.39 277	96.73 233	95.90 220	88.19 147	79.99 248	93.61 202	59.96 304	96.31 248	89.40 134	84.34 221	91.43 245

BH-w/o		92.32 129	91.79 122	93.91 170	96.85 123	86.18 218	99.11 70	95.74 227	88.13 148	84.81 191	97.00 139	77.26 182	97.91 155	89.16 140	98.03 87	97.64 162

nrg030		90.23 167	88.87 172	94.32 158	91.53 245	93.54 55	98.79 106	95.89 222	88.12 149	84.55 194	94.61 184	78.80 172	96.88 210	92.35 107	75.21 263	92.53 213

NR-MVSNet		87.74 205	86.00 210	92.96 186	91.46 246	90.68 120	96.65 236	97.42 125	88.02 150	73.42 290	93.68 199	77.31 181	95.83 268	84.26 180	71.82 300	92.36 215

tfpn111		93.20 111	92.00 117	96.83 68	97.62 94	94.84 24	99.06 74	99.36 2	87.96 151	90.47 136	96.78 145	83.29 130	98.71 129	82.93 195	90.47 179	96.94 180

conf200view11		93.32 106	92.15 113	96.84 67	97.62 94	94.84 24	99.06 74	99.36 2	87.96 151	90.47 136	96.78 145	83.29 130	98.75 124	84.11 183	90.69 172	96.94 180

thres100view900		93.34 105	92.15 113	96.90 62	97.62 94	94.84 24	99.06 74	99.36 2	87.96 151	90.47 136	96.78 145	83.29 130	98.75 124	84.11 183	90.69 172	97.12 173

thres600view7		93.18 112	92.00 117	96.75 72	97.62 94	94.92 21	99.07 72	99.36 2	87.96 151	90.47 136	96.78 145	83.29 130	98.71 129	82.93 195	90.47 179	96.61 186

CDS-MVSNet		93.47 99	93.04 95	94.76 145	94.75 190	89.45 147	98.82 101	97.03 158	87.91 155	90.97 128	96.48 158	89.06 48	96.36 238	89.50 130	92.81 146	98.49 132
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMM	86.95 13	88.77 194	88.22 187	90.43 235	93.61 214	81.34 278	98.50 140	95.92 216	87.88 156	83.85 200	95.20 177	67.20 271	97.89 157	86.90 159	84.90 216	92.06 229
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpm		89.67 178	88.95 171	91.82 205	92.54 230	81.43 275	92.95 299	95.92 216	87.81 157	90.50 135	89.44 283	84.99 111	95.65 272	83.67 190	82.71 233	98.38 139

TranMVSNet+NR-MVSNet		87.75 203	86.31 206	92.07 201	90.81 253	88.56 159	98.33 158	97.18 142	87.76 158	81.87 235	93.90 194	72.45 230	95.43 277	83.13 193	71.30 303	92.23 221

PatchMatch-RL		91.47 148	90.54 154	94.26 159	98.20 81	86.36 212	96.94 224	97.14 145	87.75 159	88.98 158	95.75 169	71.80 239	99.40 97	80.92 215	97.39 99	97.02 179

BH-RMVSNet		91.25 153	89.99 159	95.03 141	96.75 133	88.55 160	98.65 120	94.95 267	87.74 160	87.74 171	97.80 102	68.27 262	98.14 145	80.53 223	97.49 97	98.41 135

LPG-MVS_test		88.86 189	88.47 184	90.06 242	93.35 222	80.95 284	98.22 173	95.94 213	87.73 161	83.17 206	96.11 165	66.28 278	97.77 166	90.19 124	85.19 213	91.46 243

LGP-MVS_train				90.06 242	93.35 222	80.95 284		95.94 213	87.73 161	83.17 206	96.11 165	66.28 278	97.77 166	90.19 124	85.19 213	91.46 243

MVS_Test		93.67 96	92.67 101	96.69 78	96.72 134	92.66 72	97.22 216	96.03 206	87.69 163	95.12 78	94.03 189	81.55 155	98.28 142	89.17 139	96.46 107	99.14 88

ITE_SJBPF				87.93 285	92.26 233	76.44 307		93.47 295	87.67 164	79.95 249	95.49 174	56.50 311	97.38 195	75.24 269	82.33 236	89.98 287

HyFIR lowres test		93.68 95	93.29 89	94.87 143	97.57 100	88.04 168	98.18 178	98.47 24	87.57 165	91.24 125	95.05 178	85.49 106	97.46 186	93.22 97	92.82 144	99.10 90

TAMVS		92.62 126	92.09 116	94.20 161	94.10 198	87.68 174	98.41 152	96.97 162	87.53 166	89.74 149	96.04 167	84.77 116	96.49 227	88.97 141	92.31 151	98.42 134

MDTV_nov1_ep13_2view						91.17 104	91.38 312		87.45 167	93.08 103		86.67 89		87.02 156		98.95 103

XVG-ACMP-BASELINE		85.86 237	84.95 231	88.57 271	89.90 268	77.12 306	94.30 286	95.60 242	87.40 168	82.12 228	92.99 217	53.42 322	97.66 175	85.02 173	83.83 223	90.92 262

view600		92.78 117	91.50 129	96.63 81	97.51 102	94.66 34	98.91 89	99.36 2	87.31 169	89.64 152	96.59 152	83.26 135	98.63 133	80.76 218	90.15 182	96.61 186

view800		92.78 117	91.50 129	96.63 81	97.51 102	94.66 34	98.91 89	99.36 2	87.31 169	89.64 152	96.59 152	83.26 135	98.63 133	80.76 218	90.15 182	96.61 186

conf0.05thres1000		92.78 117	91.50 129	96.63 81	97.51 102	94.66 34	98.91 89	99.36 2	87.31 169	89.64 152	96.59 152	83.26 135	98.63 133	80.76 218	90.15 182	96.61 186

tfpn		92.78 117	91.50 129	96.63 81	97.51 102	94.66 34	98.91 89	99.36 2	87.31 169	89.64 152	96.59 152	83.26 135	98.63 133	80.76 218	90.15 182	96.61 186

HPM-MVS		95.41 61	95.22 57	95.99 107	99.29 45	89.14 149	99.17 57	97.09 152	87.28 173	95.40 72	98.48 87	84.93 112	99.38 98	95.64 62	99.65 30	99.47 69
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
无先验							98.52 136	97.82 63	87.20 174				99.90 31	87.64 151		99.85 21

VDD-MVS		91.24 154	90.18 157	94.45 154	97.08 117	85.84 232	98.40 155	96.10 204	86.99 175	93.36 100	98.16 97	54.27 319	99.20 106	96.59 43	90.63 176	98.31 145

WR-MVS		88.54 198	87.22 198	92.52 195	91.93 240	89.50 145	98.56 132	97.84 61	86.99 175	81.87 235	93.81 196	74.25 210	95.92 267	85.29 170	74.43 270	92.12 226

Effi-MVS+		93.87 89	93.15 92	96.02 106	95.79 156	90.76 117	96.70 234	95.78 225	86.98 177	95.71 67	97.17 131	79.58 164	98.01 153	94.57 79	96.09 117	99.31 76

CostFormer		92.89 116	92.48 105	94.12 163	94.99 184	85.89 228	92.89 300	97.00 161	86.98 177	95.00 80	90.78 246	90.05 40	97.51 185	92.92 102	91.73 162	98.96 99

VPA-MVSNet		89.10 185	87.66 191	93.45 177	92.56 229	91.02 110	97.97 191	98.32 27	86.92 179	86.03 185	92.01 226	68.84 259	97.10 204	90.92 117	75.34 262	92.23 221

MVSFormer		94.71 74	94.08 74	96.61 85	95.05 182	94.87 22	97.77 199	96.17 201	86.84 180	98.04 23	98.52 82	85.52 103	95.99 261	89.83 126	98.97 65	98.96 99

test_djsdf		88.26 202	87.73 189	89.84 247	88.05 303	82.21 270	97.77 199	96.17 201	86.84 180	82.41 224	91.95 229	72.07 235	95.99 261	89.83 126	84.50 219	91.32 247

AdaColmap		93.82 90	93.06 93	96.10 105	99.88 1	89.07 150	98.33 158	97.55 105	86.81 182	90.39 140	98.65 75	75.09 191	99.98 9	93.32 96	97.53 96	99.26 82

mvs_anonymous		92.50 128	91.65 125	95.06 139	96.60 136	89.64 142	97.06 222	96.44 184	86.64 183	84.14 197	93.93 193	82.49 145	96.17 255	91.47 112	96.08 118	99.35 72

DP-MVS Recon		95.85 52	95.15 58	97.95 19	99.87 2	94.38 43	99.60 17	97.48 116	86.58 184	94.42 85	99.13 31	87.36 78	99.98 9	93.64 90	98.33 85	99.48 68

F-COLMAP		92.07 138	91.75 124	93.02 185	98.16 84	82.89 265	98.79 106	95.97 209	86.54 185	87.92 170	97.80 102	78.69 174	99.65 70	85.97 164	95.93 121	96.53 195

PHI-MVS		96.65 30	96.46 29	97.21 45	99.34 40	91.77 82	99.70 10	98.05 47	86.48 186	98.05 22	99.20 18	89.33 46	99.96 18	98.38 18	99.62 35	99.90 9

DeepMVS_CX				76.08 323	90.74 255	51.65 346		90.84 329	86.47 187	57.89 334	87.98 293	35.88 344	92.60 317	65.77 314	65.06 316	83.97 334

BH-untuned		91.46 149	90.84 146	93.33 179	96.51 139	84.83 246	98.84 100	95.50 246	86.44 188	83.50 201	96.70 149	75.49 190	97.77 166	86.78 161	97.81 89	97.40 168

CNLPA		93.64 97	92.74 99	96.36 96	98.96 62	90.01 136	99.19 53	95.89 222	86.22 189	89.40 156	98.85 61	80.66 162	99.84 46	88.57 143	96.92 103	99.24 83

OurMVSNet-221017-0		84.13 261	83.59 247	85.77 301	87.81 305	70.24 325	94.89 281	93.65 293	86.08 190	76.53 275	93.28 210	61.41 299	96.14 257	80.95 214	77.69 255	90.93 261

FMVSNet3		88.81 193	87.08 200	93.99 168	96.52 138	94.59 38	98.08 186	96.20 199	85.85 191	82.12 228	91.60 233	74.05 213	95.40 279	79.04 231	80.24 241	91.99 231

HPM-MVS_fast		94.89 67	94.62 64	95.70 117	99.11 54	88.44 163	99.14 66	97.11 148	85.82 192	95.69 68	98.47 88	83.46 126	99.32 104	93.16 98	99.63 34	99.35 72

旧先验2							98.67 118		85.75 193	98.96 5			98.97 118	93.84 86

ab-mvs		91.05 156	89.17 167	96.69 78	95.96 153	91.72 85	92.62 302	97.23 138	85.61 194	89.74 149	93.89 195	68.55 260	99.42 95	91.09 114	87.84 199	98.92 105

新几何1				97.40 39	98.92 64	92.51 78		97.77 72	85.52 195	96.69 55	99.06 38	88.08 65	99.89 34	84.88 174	99.62 35	99.79 26

TR-MVS		90.77 161	89.44 163	94.76 145	96.31 143	88.02 169	97.92 192	95.96 211	85.52 195	88.22 163	97.23 126	66.80 274	98.09 147	84.58 177	92.38 149	98.17 150

tfpn_ndepth		93.28 108	92.32 106	96.16 103	97.74 90	92.86 70	99.01 80	98.19 39	85.50 197	89.84 148	97.12 133	93.57 9	97.58 180	79.39 229	90.50 178	98.04 152

1121		95.19 64	94.45 66	97.42 37	98.88 66	92.58 76	96.22 252	97.75 73	85.50 197	96.86 47	99.01 46	88.59 56	99.90 31	87.64 151	99.60 38	99.79 26

CP-MVSNet		86.54 227	85.45 224	89.79 249	91.02 252	82.78 268	97.38 209	97.56 104	85.37 199	79.53 255	93.03 215	71.86 238	95.25 282	79.92 224	73.43 285	91.34 246

EU-MVSNet		84.19 258	84.42 241	83.52 310	88.64 297	67.37 330	96.04 260	95.76 226	85.29 200	78.44 267	93.18 212	70.67 245	91.48 329	75.79 266	75.98 258	91.70 235

testdata				95.26 132	98.20 81	87.28 188		97.60 95	85.21 201	98.48 11	99.15 27	88.15 63	98.72 128	90.29 123	99.45 47	99.78 30

IterMVS-LS		88.34 199	87.44 193	91.04 223	94.10 198	85.85 231	98.10 184	95.48 248	85.12 202	82.03 232	91.21 236	81.35 158	95.63 273	83.86 188	75.73 260	91.63 237
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		89.87 176	89.38 165	91.36 219	94.32 195	85.87 229	97.61 204	96.59 173	85.10 203	85.51 188	97.10 134	81.30 159	96.56 219	83.85 189	83.03 230	91.64 236

IterMVS		85.81 239	84.67 237	89.22 260	93.51 216	83.67 256	96.32 246	94.80 269	85.09 204	78.69 260	90.17 277	66.57 277	93.17 302	79.48 228	77.42 256	90.81 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PCF-MVS	89.78 5	91.26 151	89.63 161	96.16 103	95.44 166	91.58 90	95.29 278	96.10 204	85.07 205	82.75 216	97.45 114	78.28 176	99.78 57	80.60 222	95.65 126	97.12 173
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
semantic-postprocess				89.00 265	93.46 219	82.90 264		94.70 272	85.02 206	78.62 262	90.35 266	66.63 275	93.33 301	79.38 230	77.36 257	90.76 268

Fast-Effi-MVS+		91.72 145	90.79 149	94.49 152	95.89 154	87.40 183	99.54 23	95.70 231	85.01 207	89.28 157	95.68 170	77.75 179	97.57 184	83.22 191	95.06 129	98.51 131

WR-MVS_H		86.53 228	85.49 223	89.66 253	91.04 251	83.31 259	97.53 206	98.20 32	84.95 208	79.64 252	90.90 245	78.01 178	95.33 280	76.29 256	72.81 287	90.35 278

MVS		93.92 87	92.28 108	98.83 2	95.69 160	96.82 3	96.22 252	98.17 41	84.89 209	84.34 196	98.61 78	79.32 167	99.83 48	93.88 85	99.43 48	99.86 20

PS-CasMVS		85.81 239	84.58 238	89.49 257	90.77 254	82.11 271	97.20 217	97.36 131	84.83 210	79.12 259	92.84 218	67.42 270	95.16 284	78.39 238	73.25 286	91.21 250

dp		90.16 170	88.83 174	94.14 162	96.38 142	86.42 208	91.57 311	97.06 155	84.76 211	88.81 159	90.19 276	84.29 119	97.43 188	75.05 270	91.35 169	98.56 129

UnsupCasMVSNet_eth		78.90 295	76.67 297	85.58 302	82.81 327	74.94 310	91.98 308	96.31 190	84.64 212	65.84 325	87.71 296	51.33 325	92.23 322	72.89 295	56.50 337	89.56 294

v2v482		87.27 212	85.76 216	91.78 210	89.59 281	87.58 176	98.56 132	95.54 243	84.53 213	82.51 221	91.78 230	73.11 225	96.47 230	82.07 202	74.14 278	91.30 248

EPP-MVSNet		93.75 92	93.67 85	94.01 167	95.86 155	85.70 234	98.67 118	97.66 83	84.46 214	91.36 123	97.18 129	91.16 17	97.79 164	92.93 101	93.75 138	98.53 130

PEN-MVS		85.21 246	83.93 246	89.07 264	89.89 269	81.31 279	97.09 221	97.24 137	84.45 215	78.66 261	92.68 220	68.44 261	94.87 289	75.98 258	70.92 304	91.04 259

tpmp4_e23		91.05 156	90.07 158	93.97 169	95.77 158	85.30 239	92.64 301	97.09 152	84.42 216	91.53 119	90.31 268	87.38 74	97.82 162	80.86 217	90.62 177	98.79 114

SixPastTwentyTwo		82.63 267	81.58 266	85.79 300	88.12 302	71.01 324	95.17 279	92.54 312	84.33 217	72.93 295	92.08 223	60.41 303	95.61 274	74.47 275	74.15 277	90.75 269

testing_2		80.92 285	77.24 293	91.98 202	78.88 335	87.83 171	93.96 291	95.72 229	84.27 218	56.20 336	80.42 327	38.64 342	96.40 235	87.20 153	79.85 245	91.72 234

tfpn1000		92.67 124	91.64 126	95.78 115	97.61 99	92.34 79	98.69 113	98.18 40	84.15 219	88.80 160	96.99 140	93.56 10	97.21 199	76.56 254	90.19 181	97.77 161

XXY-MVS		87.75 203	86.02 209	92.95 187	90.46 257	89.70 141	97.71 201	95.90 220	84.02 220	80.95 240	94.05 186	67.51 269	97.10 204	85.16 171	78.41 250	92.04 230

tpm2		91.77 144	91.09 134	93.82 173	94.83 188	85.56 237	92.51 303	97.16 144	84.00 221	93.83 97	90.66 256	87.54 71	97.17 200	87.73 150	91.55 165	98.72 120

anonymousdsp		86.69 223	85.75 218	89.53 255	86.46 317	82.94 262	96.39 243	95.71 230	83.97 222	79.63 253	90.70 249	68.85 258	95.94 264	86.01 163	84.02 222	89.72 291

v1141		87.23 214	85.75 218	91.67 211	89.88 270	87.43 182	98.52 136	95.62 239	83.91 223	82.83 215	90.69 251	74.70 193	96.49 227	81.53 212	74.08 279	91.07 256

divwei89l23v2f112		87.23 214	85.75 218	91.66 212	89.88 270	87.40 183	98.53 135	95.62 239	83.91 223	82.84 214	90.67 254	74.75 192	96.49 227	81.55 210	74.05 281	91.08 254

v1		87.23 214	85.76 216	91.66 212	89.88 270	87.37 185	98.54 134	95.64 238	83.91 223	82.88 213	90.70 249	74.64 194	96.53 223	81.54 211	74.08 279	91.08 254

v6		87.27 212	85.86 214	91.50 215	89.97 265	86.84 198	98.45 146	95.67 233	83.85 226	83.11 208	90.97 242	74.46 203	96.58 217	81.97 205	74.34 272	91.09 253

v1neww		87.29 210	85.88 212	91.50 215	90.07 258	86.87 194	98.45 146	95.66 236	83.84 227	83.07 209	90.99 240	74.58 200	96.56 219	81.96 206	74.33 273	91.07 256

v7new		87.29 210	85.88 212	91.50 215	90.07 258	86.87 194	98.45 146	95.66 236	83.84 227	83.07 209	90.99 240	74.58 200	96.56 219	81.96 206	74.33 273	91.07 256

v148		86.38 230	85.06 228	90.37 237	89.47 287	84.10 252	98.52 136	95.48 248	83.80 229	80.93 241	90.22 273	74.60 198	96.31 248	80.92 215	71.55 301	90.69 272

MS-PatchMatch		86.75 222	85.92 211	89.22 260	91.97 237	82.47 269	96.91 225	96.14 203	83.74 230	77.73 270	93.53 205	58.19 306	97.37 197	76.75 252	98.35 84	87.84 304

test222					98.32 79	91.21 101	98.08 186	97.58 98	83.74 230	95.87 64	99.02 42	86.74 88			99.64 31	99.81 23

K. test v3		81.04 283	79.77 277	84.83 305	87.41 312	70.23 326	95.60 275	93.93 288	83.70 232	67.51 320	89.35 285	55.76 312	93.58 300	76.67 253	68.03 311	90.67 273

V42		87.00 218	85.68 221	90.98 225	89.91 266	86.08 222	98.32 160	95.61 241	83.67 233	82.72 217	90.67 254	74.00 214	96.53 223	81.94 208	74.28 276	90.32 279

API-MVS		94.78 70	94.18 71	96.59 86	99.21 50	90.06 134	98.80 103	97.78 71	83.59 234	93.85 96	99.21 17	83.79 122	99.97 14	92.37 106	99.00 64	99.74 39

conf0.01		92.06 140	90.99 136	95.24 133	96.84 124	91.39 93	98.31 161	98.20 32	83.57 235	88.08 164	97.34 117	91.05 20	97.40 189	75.80 260	89.74 189	96.94 180

conf0.002		92.06 140	90.99 136	95.24 133	96.84 124	91.39 93	98.31 161	98.20 32	83.57 235	88.08 164	97.34 117	91.05 20	97.40 189	75.80 260	89.74 189	96.94 180

thresconf0.02		92.14 133	90.99 136	95.58 121	96.84 124	91.39 93	98.31 161	98.20 32	83.57 235	88.08 164	97.34 117	91.05 20	97.40 189	75.80 260	89.74 189	97.94 156

tfpn_n400		92.14 133	90.99 136	95.58 121	96.84 124	91.39 93	98.31 161	98.20 32	83.57 235	88.08 164	97.34 117	91.05 20	97.40 189	75.80 260	89.74 189	97.94 156

tfpnconf		92.14 133	90.99 136	95.58 121	96.84 124	91.39 93	98.31 161	98.20 32	83.57 235	88.08 164	97.34 117	91.05 20	97.40 189	75.80 260	89.74 189	97.94 156

tfpnview11		92.14 133	90.99 136	95.58 121	96.84 124	91.39 93	98.31 161	98.20 32	83.57 235	88.08 164	97.34 117	91.05 20	97.40 189	75.80 260	89.74 189	97.94 156

DTE-MVSNet		84.14 260	82.80 254	88.14 281	88.95 292	79.87 290	96.81 228	96.24 197	83.50 241	77.60 272	92.52 222	67.89 267	94.24 297	72.64 296	69.05 308	90.32 279

LFMVS		92.23 132	90.84 146	96.42 93	98.24 80	91.08 109	98.24 171	96.22 198	83.39 242	94.74 83	98.31 92	61.12 301	98.85 119	94.45 81	92.82 144	99.32 75

LF4IMVS		81.94 272	81.17 271	84.25 308	87.23 314	68.87 329	93.35 297	91.93 321	83.35 243	75.40 283	93.00 216	49.25 330	96.65 216	78.88 234	78.11 252	87.22 313

v1144		86.83 221	85.31 226	91.40 218	89.75 275	87.21 191	98.31 161	95.45 251	83.22 244	82.70 218	90.78 246	73.36 220	96.36 238	79.49 227	74.69 268	90.63 274

CPTT-MVS		94.60 78	94.43 67	95.09 136	99.66 12	86.85 196	99.44 31	97.47 117	83.22 244	94.34 88	98.96 51	82.50 144	99.55 79	94.81 74	99.50 43	98.88 107

Patchmatch-RL test		81.90 273	80.13 274	87.23 292	80.71 330	70.12 327	84.07 336	88.19 343	83.16 246	70.57 300	82.18 313	87.18 81	92.59 318	82.28 201	62.78 319	98.98 97

v7		86.91 219	85.45 224	91.29 220	90.06 260	86.73 200	98.26 169	95.49 247	83.08 247	82.95 212	90.96 243	73.37 219	96.42 233	79.90 225	74.97 264	90.71 271

ADS-MVSNet2		87.62 206	86.88 201	89.86 246	96.21 146	79.14 292	87.15 325	92.99 300	83.01 248	89.91 146	87.27 301	78.87 170	92.80 310	74.20 278	92.27 152	97.64 162

ADS-MVSNet		88.99 186	87.30 195	94.07 164	96.21 146	87.56 177	87.15 325	96.78 168	83.01 248	89.91 146	87.27 301	78.87 170	97.01 206	74.20 278	92.27 152	97.64 162

GBi-Net		86.67 224	84.96 229	91.80 206	95.11 179	88.81 154	96.77 229	95.25 261	82.94 250	82.12 228	90.25 270	62.89 293	94.97 286	79.04 231	80.24 241	91.62 238

test1		86.67 224	84.96 229	91.80 206	95.11 179	88.81 154	96.77 229	95.25 261	82.94 250	82.12 228	90.25 270	62.89 293	94.97 286	79.04 231	80.24 241	91.62 238

FMVSNet2		86.90 220	84.79 235	93.24 180	95.11 179	92.54 77	97.67 202	95.86 224	82.94 250	80.55 242	91.17 237	62.89 293	95.29 281	77.23 245	79.71 247	91.90 232

CSCG		94.87 68	94.71 63	95.36 128	99.54 27	86.49 206	99.34 48	98.15 43	82.71 253	90.15 143	99.25 12	89.48 45	99.86 43	94.97 72	98.82 73	99.72 42

OpenMVS	85.28 14	90.75 162	88.84 173	96.48 89	93.58 215	93.51 56	98.80 103	97.41 126	82.59 254	78.62 262	97.49 113	68.00 265	99.82 51	84.52 178	98.55 82	96.11 197

114514_t		94.06 86	93.05 94	97.06 49	99.08 56	92.26 80	98.97 84	97.01 160	82.58 255	92.57 107	98.22 94	80.68 161	99.30 105	89.34 135	99.02 63	99.63 55

pmmvs4		87.58 207	86.17 208	91.80 206	89.58 282	88.92 152	97.25 213	95.28 260	82.54 256	80.49 243	93.17 213	75.62 189	96.05 260	82.75 197	78.90 248	90.42 277

v1192		86.32 231	84.71 236	91.17 221	89.53 284	86.40 209	98.13 182	95.44 252	82.52 257	82.42 223	90.62 259	71.58 241	96.33 245	77.23 245	74.88 265	90.79 266

v144192		86.40 229	84.89 232	90.91 226	89.48 286	85.59 235	98.21 176	95.43 253	82.45 258	82.62 219	90.58 262	72.79 229	96.36 238	78.45 237	74.04 282	90.79 266

TAPA-MVS	87.50 9	90.35 165	89.05 169	94.25 160	98.48 77	85.17 242	98.42 150	96.58 176	82.44 259	87.24 177	98.53 81	82.77 142	98.84 120	59.09 328	97.88 88	98.72 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v1921920		86.02 234	84.44 240	90.77 228	89.32 288	85.20 240	98.10 184	95.35 258	82.19 260	82.25 226	90.71 248	70.73 244	96.30 251	76.85 251	74.49 269	90.80 265

MVP-Stereo		86.61 226	85.83 215	88.93 266	88.70 296	83.85 255	96.07 259	94.41 281	82.15 261	75.64 282	91.96 228	67.65 268	96.45 232	77.20 247	98.72 76	86.51 317
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v8		86.11 233	84.45 239	91.10 222	89.99 264	86.85 196	97.24 214	95.36 256	81.99 262	79.89 250	89.86 279	74.53 202	96.39 236	78.83 235	72.32 293	90.05 285

tpmvs		89.16 184	87.76 188	93.35 178	97.19 113	84.75 247	90.58 320	97.36 131	81.99 262	84.56 193	89.31 286	83.98 121	98.17 144	74.85 273	90.00 187	97.12 173

pm-mvs1		84.68 249	82.78 255	90.40 236	89.58 282	85.18 241	97.31 210	94.73 271	81.93 264	76.05 278	92.01 226	65.48 283	96.11 258	78.75 236	69.14 307	89.91 288

v1240		85.77 241	84.11 243	90.73 229	89.26 289	85.15 243	97.88 195	95.23 265	81.89 265	82.16 227	90.55 264	69.60 253	96.31 248	75.59 268	74.87 266	90.72 270

diffmvs		92.07 138	90.77 150	95.97 109	96.41 141	91.32 100	96.46 241	95.98 207	81.73 266	94.33 89	93.36 207	78.72 173	98.20 143	84.28 179	95.66 125	98.41 135

test20.03		78.51 298	77.48 291	81.62 316	83.07 326	71.03 323	96.11 258	92.83 309	81.66 267	69.31 305	89.68 281	57.53 307	87.29 336	58.65 329	68.47 309	86.53 316

pmmvs5		85.87 236	84.40 242	90.30 238	88.53 298	84.23 251	98.60 128	93.71 291	81.53 268	80.29 245	92.02 225	64.51 286	95.52 275	82.04 204	78.34 251	91.15 251

MIMVSNet		84.48 254	81.83 262	92.42 196	91.73 243	87.36 186	85.52 328	94.42 280	81.40 269	81.91 233	87.58 297	51.92 324	92.81 309	73.84 283	88.15 198	97.08 177

v10		85.73 242	84.01 245	90.87 227	90.03 261	86.73 200	97.20 217	95.22 266	81.25 270	79.85 251	89.75 280	73.30 224	96.28 252	76.87 249	72.64 289	89.61 293

ACMH+	83.78 15	84.21 256	82.56 260	89.15 262	93.73 213	79.16 291	96.43 242	94.28 283	81.09 271	74.00 289	94.03 189	54.58 318	97.67 174	76.10 257	78.81 249	90.63 274

ACMH	83.09 17	84.60 251	82.61 258	90.57 231	93.18 225	82.94 262	96.27 247	94.92 268	81.01 272	72.61 298	93.61 202	56.54 310	97.79 164	74.31 276	81.07 240	90.99 260
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PM-MVS		74.88 306	72.85 307	80.98 318	78.98 334	64.75 331	90.81 317	85.77 346	80.95 273	68.23 312	82.81 311	29.08 346	92.84 307	76.54 255	62.46 321	85.36 330

QAPM		91.41 150	89.49 162	97.17 47	95.66 162	93.42 58	98.60 128	97.51 111	80.92 274	81.39 239	97.41 116	72.89 228	99.87 38	82.33 200	98.68 78	98.21 148

v7n		84.42 255	82.75 256	89.43 258	88.15 301	81.86 272	96.75 232	95.67 233	80.53 275	78.38 268	89.43 284	69.89 248	96.35 243	73.83 284	72.13 297	90.07 284

cascas		90.93 159	89.33 166	95.76 116	95.69 160	93.03 66	98.99 83	96.59 173	80.49 276	86.79 183	94.45 185	65.23 284	98.60 137	93.52 92	92.18 155	95.66 199

Baseline_NR-MVSNet		85.83 238	84.82 234	88.87 267	88.73 295	83.34 258	98.63 123	91.66 323	80.41 277	82.44 222	91.35 235	74.63 196	95.42 278	84.13 182	71.39 302	87.84 304

v748		83.84 263	82.31 261	88.41 276	87.65 309	79.10 293	96.66 235	95.51 245	80.09 278	77.65 271	88.53 292	69.81 249	96.23 253	75.67 267	69.25 306	89.91 288

V4		84.20 257	82.92 251	88.02 282	87.59 311	79.91 289	96.21 255	95.36 256	79.88 279	78.51 265	89.00 288	69.52 254	96.32 246	77.96 240	72.29 294	87.83 306

v52		84.19 258	82.92 251	88.01 283	87.64 310	79.92 288	96.23 250	95.32 259	79.87 280	78.51 265	89.05 287	69.50 255	96.32 246	77.95 241	72.24 296	87.79 307

testus		77.11 303	76.95 296	77.58 322	80.02 332	58.93 338	97.78 197	90.48 332	79.68 281	72.84 296	90.61 261	37.72 343	86.57 339	60.28 326	83.18 229	87.23 312

Anonymous20231206		80.76 286	79.42 282	84.79 306	84.78 320	72.98 317	96.53 238	92.97 301	79.56 282	74.33 286	88.83 289	61.27 300	92.15 323	60.59 324	75.92 259	89.24 297

test2356		80.96 284	81.77 264	78.52 321	81.02 329	62.33 332	98.22 173	94.49 277	79.38 283	74.56 285	90.34 267	70.65 247	85.10 340	60.83 322	86.42 203	88.14 301

DSMNet-mixed		81.60 277	81.43 268	82.10 313	84.36 322	60.79 334	93.63 295	86.74 344	79.00 284	79.32 257	87.15 303	63.87 289	89.78 331	66.89 310	91.92 158	95.73 198

LTVRE_ROB	81.71 19	84.59 252	82.72 257	90.18 239	92.89 228	83.18 260	93.15 298	94.74 270	78.99 285	75.14 284	92.69 219	65.64 282	97.63 177	69.46 303	81.82 238	89.74 290
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
ppachtmachnet_test		83.63 265	81.57 267	89.80 248	89.01 291	85.09 244	97.13 220	94.50 276	78.84 286	76.14 277	91.00 239	69.78 250	94.61 295	63.40 317	74.36 271	89.71 292

TransMVSNet (Re)		81.97 271	79.61 280	89.08 263	89.70 277	84.01 253	97.26 212	91.85 322	78.84 286	73.07 294	91.62 232	67.17 272	95.21 283	67.50 307	59.46 333	88.02 303

v18		82.00 270	79.76 278	88.72 268	90.03 261	86.81 199	96.17 257	93.12 297	78.70 288	68.39 307	82.10 314	74.64 194	93.00 303	74.21 277	60.45 326	86.35 318

v17		81.87 275	79.61 280	88.64 270	89.91 266	86.64 204	96.01 261	93.08 298	78.54 289	68.27 309	81.96 316	74.44 204	92.95 305	74.03 280	60.22 328	86.34 319

v16		81.90 273	79.65 279	88.65 269	90.02 263	86.66 203	96.01 261	93.07 299	78.53 290	68.27 309	82.05 315	74.39 206	92.96 304	74.02 281	60.48 325	86.33 320

tfpnnormal		83.65 264	81.35 269	90.56 232	91.37 248	88.06 167	97.29 211	97.87 59	78.51 291	76.20 276	90.91 244	64.78 285	96.47 230	61.71 321	73.50 283	87.13 314

v15		81.62 276	79.32 283	88.52 272	89.80 273	86.56 205	95.83 270	92.96 302	78.50 292	67.88 313	81.68 318	74.22 211	92.82 308	73.46 287	59.55 329	86.18 323

V14		81.55 278	79.26 284	88.42 275	89.80 273	86.33 213	95.72 273	92.96 302	78.35 293	67.82 314	81.70 317	74.13 212	92.78 312	73.32 288	59.50 331	86.16 325

V9		81.46 279	79.15 285	88.39 278	89.75 275	86.17 219	95.62 274	92.92 304	78.22 294	67.65 318	81.64 319	73.95 215	92.80 310	73.15 291	59.43 334	86.21 322

v12		81.37 281	79.05 286	88.33 279	89.68 278	86.05 225	95.48 276	92.92 304	78.08 295	67.55 319	81.58 320	73.75 216	92.75 313	73.05 292	59.37 335	86.18 323

v11		81.38 280	79.03 287	88.41 276	89.68 278	86.43 207	95.74 272	92.82 311	78.03 296	67.74 315	81.45 322	73.33 223	92.69 316	72.23 298	60.27 327	86.11 327

FMVSNet1		83.94 262	81.32 270	91.80 206	91.94 239	88.81 154	96.77 229	95.25 261	77.98 297	78.25 269	90.25 270	50.37 328	94.97 286	73.27 289	77.81 254	91.62 238

pmmvs-eth3d		78.71 297	76.16 299	86.38 296	80.25 331	81.19 281	94.17 288	92.13 318	77.97 298	66.90 323	82.31 312	55.76 312	92.56 319	73.63 286	62.31 322	85.38 329

AllTest		84.97 247	83.12 249	90.52 233	96.82 130	78.84 296	95.89 264	92.17 316	77.96 299	75.94 279	95.50 172	55.48 314	99.18 107	71.15 299	87.14 201	93.55 206

TestCases				90.52 233	96.82 130	78.84 296		92.17 316	77.96 299	75.94 279	95.50 172	55.48 314	99.18 107	71.15 299	87.14 201	93.55 206

v13		81.30 282	78.99 288	88.25 280	89.61 280	85.87 229	95.39 277	92.90 306	77.93 301	67.45 322	81.52 321	73.66 217	92.75 313	72.91 294	59.53 330	86.14 326

MSDG		88.29 201	86.37 205	94.04 166	96.90 122	86.15 220	96.52 239	94.36 282	77.89 302	79.22 258	96.95 142	69.72 251	99.59 78	73.20 290	92.58 148	96.37 196

new-patchmatchnet		74.80 307	72.40 308	81.99 314	78.36 336	72.20 320	94.44 283	92.36 314	77.06 303	63.47 327	79.98 331	51.04 326	88.85 333	60.53 325	54.35 339	84.92 332

FMVSNet5		82.29 268	80.54 273	87.52 289	93.79 212	84.01 253	93.73 293	92.47 313	76.92 304	74.27 287	86.15 309	63.69 290	89.24 332	69.07 304	74.79 267	89.29 296

VDDNet		90.08 173	88.54 183	94.69 148	94.41 194	87.68 174	98.21 176	96.40 185	76.21 305	93.33 101	97.75 105	54.93 317	98.77 122	94.71 77	90.96 170	97.61 166

tpm cat1		88.89 188	87.27 196	93.76 174	95.79 156	85.32 238	90.76 318	97.09 152	76.14 306	85.72 186	88.59 291	82.92 140	98.04 151	76.96 248	91.43 166	97.90 160

MDA-MVSNet-bldmvs		77.82 301	74.75 303	87.03 293	88.33 299	78.52 299	96.34 245	92.85 308	75.57 307	48.87 341	87.89 294	57.32 309	92.49 320	60.79 323	64.80 317	90.08 283

TinyColmap		80.42 289	77.94 289	87.85 286	92.09 236	78.58 298	93.74 292	89.94 336	74.99 308	69.77 304	91.78 230	46.09 332	97.58 180	65.17 315	77.89 253	87.38 309

LS3D		90.19 169	88.72 175	94.59 150	98.97 60	86.33 213	96.90 226	96.60 172	74.96 309	84.06 199	98.74 68	75.78 188	99.83 48	74.93 271	97.57 94	97.62 165

EG-PatchMatch MVS		79.92 290	77.59 290	86.90 294	87.06 315	77.90 305	96.20 256	94.06 287	74.61 310	66.53 324	88.76 290	40.40 341	96.20 254	67.02 309	83.66 226	86.61 315

TDRefinement		78.01 299	75.31 300	86.10 299	70.06 343	73.84 314	93.59 296	91.58 325	74.51 311	73.08 293	91.04 238	49.63 329	97.12 201	74.88 272	59.47 332	87.33 310

RPSCF		85.33 245	85.55 222	84.67 307	94.63 192	62.28 333	93.73 293	93.76 289	74.38 312	85.23 190	97.06 137	64.09 287	98.31 140	80.98 213	86.08 209	93.41 208

MDA-MVSNet_test_wron		79.65 292	77.05 294	87.45 290	87.79 307	80.13 286	96.25 249	94.44 278	73.87 313	51.80 339	87.47 300	68.04 264	92.12 324	66.02 312	67.79 312	90.09 282

YYNet1		79.64 293	77.04 295	87.43 291	87.80 306	79.98 287	96.23 250	94.44 278	73.83 314	51.83 338	87.53 299	67.96 266	92.07 325	66.00 313	67.75 313	90.23 281

MIMVSNet1		75.92 305	73.30 306	83.81 309	81.29 328	75.57 309	92.26 306	92.05 319	73.09 315	67.48 321	86.18 308	40.87 339	87.64 335	55.78 331	70.68 305	88.21 300

Patchmatch-test		86.25 232	84.06 244	92.82 188	94.42 193	82.88 266	82.88 340	94.23 284	71.58 316	79.39 256	90.62 259	89.00 50	96.42 233	63.03 318	91.37 168	99.16 87

COLMAP_ROB	82.69 18	84.54 253	82.82 253	89.70 251	96.72 134	78.85 295	95.89 264	92.83 309	71.55 317	77.54 273	95.89 168	59.40 305	99.14 112	67.26 308	88.26 197	91.11 252
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testpf		80.59 287	80.13 274	81.97 315	94.25 197	71.65 322	60.37 350	95.46 250	70.99 318	76.97 274	87.74 295	73.58 218	91.67 327	76.86 250	84.97 215	82.60 338

PatchT		85.44 244	83.19 248	92.22 198	93.13 226	83.00 261	83.80 338	96.37 186	70.62 319	90.55 134	79.63 332	84.81 115	94.87 289	58.18 330	91.59 164	98.79 114

DP-MVS		88.75 195	86.56 203	95.34 129	98.92 64	87.45 180	97.64 203	93.52 294	70.55 320	81.49 237	97.25 124	74.43 205	99.88 35	71.14 301	94.09 136	98.67 123

new_pmnet		76.02 304	73.71 305	82.95 311	83.88 324	72.85 318	91.26 314	92.26 315	70.44 321	62.60 328	81.37 323	47.64 331	92.32 321	61.85 320	72.10 298	83.68 335

test1235678		71.07 311	69.53 313	75.71 324	71.87 342	55.27 344	94.32 284	90.76 330	70.23 322	57.61 335	79.06 333	43.13 335	83.72 342	50.48 335	68.30 310	88.14 301

N_pmnet		70.19 312	69.87 311	71.12 327	88.24 300	30.63 358	95.85 269	28.70 359	70.18 323	68.73 306	86.55 307	64.04 288	93.81 298	53.12 334	73.46 284	88.94 299

UnsupCasMVSNet_bld		73.85 308	70.14 310	84.99 304	79.44 333	75.73 308	88.53 323	95.24 264	70.12 324	61.94 329	74.81 337	41.41 338	93.62 299	68.65 305	51.13 344	85.62 328

JIA-IIPM		85.97 235	84.85 233	89.33 259	93.23 224	73.68 315	85.05 331	97.13 147	69.62 325	91.56 118	68.03 341	88.03 66	96.96 207	77.89 242	93.12 141	97.34 170

Patchmtry		83.61 266	81.64 265	89.50 256	93.36 221	82.84 267	84.10 335	94.20 285	69.47 326	79.57 254	86.88 305	84.43 117	94.78 293	68.48 306	74.30 275	90.88 263

test_0402		78.81 296	76.33 298	86.26 297	91.18 249	78.44 300	95.88 266	91.34 327	68.55 327	70.51 302	89.91 278	52.65 323	94.99 285	47.14 338	79.78 246	85.34 331

CMPMVS	58.40 21	80.48 288	80.11 276	81.59 317	85.10 319	59.56 336	94.14 289	95.95 212	68.54 328	60.71 330	93.31 208	55.35 316	97.87 159	83.06 194	84.85 217	87.33 310
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
gg-mvs-nofinetune		90.00 174	87.71 190	96.89 66	96.15 149	94.69 33	85.15 330	97.74 75	68.32 329	92.97 106	60.16 343	96.10 3	96.84 211	93.89 84	98.87 69	99.14 88

pmmvs6		79.90 291	77.31 292	87.67 288	84.17 323	78.13 302	95.86 268	93.68 292	67.94 330	72.67 297	89.62 282	50.98 327	95.75 270	74.80 274	66.04 314	89.14 298

OpenMVS_ROB	73.86 20	77.99 300	75.06 302	86.77 295	83.81 325	77.94 304	96.38 244	91.53 326	67.54 331	68.38 308	87.13 304	43.94 334	96.08 259	55.03 332	81.83 237	86.29 321

test12356		66.36 314	65.12 314	70.08 330	66.92 344	50.46 347	89.96 321	88.58 341	66.00 332	53.38 337	78.13 336	32.89 345	82.87 343	48.36 337	61.87 323	76.92 340

LP		77.80 302	74.39 304	88.01 283	91.93 240	79.02 294	80.88 342	92.90 306	65.43 333	72.00 299	81.29 324	65.78 280	92.73 315	43.76 343	75.58 261	92.27 218

ANet_high		50.71 323	46.17 324	64.33 333	44.27 356	52.30 345	76.13 345	78.73 351	64.95 334	27.37 350	55.23 348	14.61 356	67.74 352	36.01 348	18.23 351	72.95 344

RPMNet		84.62 250	81.78 263	93.16 182	93.47 217	86.24 215	84.97 332	96.28 195	64.85 335	90.76 131	78.80 334	80.95 160	94.82 291	53.76 333	92.17 156	98.41 135

pmmvs3		72.86 309	69.76 312	82.17 312	73.86 338	74.19 313	94.20 287	89.01 339	64.23 336	67.72 316	80.91 326	41.48 337	88.65 334	62.40 319	54.02 340	83.68 335

1111		72.28 310	71.36 309	75.02 325	73.04 339	57.38 340	92.30 304	90.22 334	62.27 337	59.46 331	80.36 328	76.23 185	87.07 337	44.29 341	64.08 318	80.59 339

.test1245		61.50 316	64.44 315	52.65 340	73.04 339	57.38 340	92.30 304	90.22 334	62.27 337	59.46 331	80.36 328	76.23 185	87.07 337	44.29 341	1.80 355	13.50 355

no-one		56.69 320	51.89 323	71.08 328	59.35 351	58.65 339	83.78 339	84.81 349	61.73 339	36.46 347	56.52 347	18.15 353	84.78 341	47.03 339	19.19 349	69.81 345

testmv		60.41 317	57.98 318	67.69 331	58.16 352	47.14 349	89.09 322	86.74 344	61.52 340	44.30 343	68.44 339	20.98 349	79.92 348	40.94 345	51.67 342	76.01 341

MVS-HIRNet		79.01 294	75.13 301	90.66 230	93.82 211	81.69 274	85.16 329	93.75 290	54.54 341	74.17 288	59.15 345	57.46 308	96.58 217	63.74 316	94.38 133	93.72 205

Anonymous20231211		67.10 313	63.29 316	78.54 320	75.68 337	60.00 335	92.05 307	88.86 340	49.84 342	59.35 333	78.48 335	26.15 347	90.76 330	45.96 340	53.24 341	84.88 333

PMMVS2		58.97 319	55.07 320	70.69 329	62.72 345	55.37 343	85.97 327	80.52 350	49.48 343	45.94 342	68.31 340	15.73 355	80.78 346	49.79 336	37.12 345	75.91 342

FPMVS		61.57 315	60.32 317	65.34 332	60.14 349	42.44 352	91.02 316	89.72 337	44.15 344	42.63 344	80.93 325	19.02 350	80.59 347	42.50 344	72.76 288	73.00 343

LCM-MVSNet		60.07 318	56.37 319	71.18 326	54.81 353	48.67 348	82.17 341	89.48 338	37.95 345	49.13 340	69.12 338	13.75 357	81.76 344	59.28 327	51.63 343	83.10 337

PNet_i23d		48.05 324	44.98 325	57.28 336	60.15 347	42.39 353	80.85 343	73.14 355	36.78 346	27.46 349	56.66 346	6.38 358	68.34 351	36.65 347	26.72 347	61.10 347

Gipuma		54.77 321	52.22 322	62.40 334	86.50 316	59.37 337	50.20 351	90.35 333	36.52 347	41.20 345	49.49 349	18.33 352	81.29 345	32.10 349	65.34 315	46.54 351
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
wuykxyi23d		43.53 326	37.95 329	60.27 335	45.36 355	44.79 350	68.27 347	74.26 354	33.48 348	18.21 355	40.16 356	3.64 360	71.01 350	38.85 346	19.31 348	65.02 346

PMVS	41.42 23	45.67 325	42.50 326	55.17 338	34.28 357	32.37 356	66.24 348	78.71 352	30.72 349	22.04 353	59.59 344	4.59 359	77.85 349	27.49 350	58.84 336	55.29 349
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		41.02 328	40.93 327	41.29 341	61.97 346	33.83 355	84.00 337	65.17 357	27.17 350	27.56 348	46.72 351	17.63 354	60.41 355	19.32 352	18.82 350	29.61 352

EMVS		39.96 329	39.88 328	40.18 342	59.57 350	32.12 357	84.79 334	64.57 358	26.27 351	26.14 351	44.18 354	18.73 351	59.29 356	17.03 353	17.67 352	29.12 353

MVE	44.00 22	41.70 327	37.64 330	53.90 339	49.46 354	43.37 351	65.09 349	66.66 356	26.19 352	25.77 352	48.53 350	3.58 362	63.35 354	26.15 351	27.28 346	54.97 350
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		53.66 322	52.86 321	56.05 337	32.75 358	41.97 354	73.42 346	76.12 353	21.91 353	39.68 346	96.39 162	42.59 336	65.10 353	78.00 239	14.92 353	61.08 348

wuyk23d		16.71 333	16.73 335	16.65 344	60.15 347	25.22 359	41.24 352	5.17 360	6.56 354	5.48 357	3.61 358	3.64 360	22.72 357	15.20 354	9.52 354	1.99 357

testmvs		18.81 332	23.05 333	6.10 346	4.48 359	2.29 361	97.78 197	3.00 361	3.27 355	18.60 354	62.71 342	1.53 364	2.49 359	14.26 355	1.80 355	13.50 355

test123		16.58 334	19.47 334	7.91 345	3.59 360	5.37 360	94.32 284	1.39 362	2.49 356	13.98 356	44.60 353	2.91 363	2.65 358	11.35 356	0.57 357	15.70 354

cdsmvs_eth3d_5k		22.52 331	30.03 332	0.00 347	0.00 361	0.00 362	0.00 353	97.17 143	0.00 357	0.00 358	98.77 65	74.35 207	0.00 360	0.00 357	0.00 358	0.00 358

pcd_1.5k_mvsjas		6.87 336	9.16 337	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	82.48 146	0.00 360	0.00 357	0.00 358	0.00 358

pcd1.5k->3k		35.91 330	37.64 330	30.74 343	89.49 285	0.00 362	0.00 353	96.36 189	0.00 357	0.00 358	0.00 359	69.17 256	0.00 360	0.00 357	83.71 225	92.21 223

sosnet-low-res		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

sosnet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uncertanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

Regformer		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

ab-mvs-re		8.21 335	10.94 336	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	98.50 84	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

GSMVS																98.84 109

test_part2					99.54 27	95.42 14				98.13 17

test_part1								97.69 79				93.96 6			99.83 12	99.90 9

sam_mvs1												88.39 59				98.84 109

sam_mvs												87.08 82

ambc				79.60 319	72.76 341	56.61 342	76.20 344	92.01 320		68.25 311	80.23 330	23.34 348	94.73 294	73.78 285	60.81 324	87.48 308

MTGPA								97.45 119

test_post1							90.74 319				41.37 355	85.38 110	96.36 238	83.16 192

test_post											46.00 352	87.37 75	97.11 202

patchmatchnet-post											84.86 310	88.73 53	96.81 213

GG-mvs-BLEND				96.98 57	96.53 137	94.81 29	87.20 324	97.74 75		93.91 95	96.40 160	96.56 2	96.94 209	95.08 69	98.95 68	99.20 86

MTMP								91.09 328

test9_res														98.60 11	99.87 5	99.90 9

agg_prior2														97.84 28	99.87 5	99.91 8

agg_prior					99.54 27	92.66 72		97.64 88		97.98 26			99.61 75

test_prior4						92.00 81	99.41 38

test_prior				97.01 51	99.58 19	91.77 82		97.57 102					99.49 87			99.79 26

新几何2							98.26 169

旧先验1					98.97 60	92.90 69		97.74 75			99.15 27	91.05 20			99.33 53	99.60 59

原ACMM2							98.69 113

testdata2													99.88 35	84.16 181

segment_acmp												90.56 35

test12				97.83 23	99.33 43	94.45 40		97.55 105		97.56 32		88.60 54	99.50 86		99.71 27	99.55 61

plane_prior7					93.84 209	85.73 233

plane_prior6					93.92 206	86.02 226						72.92 226

plane_prior5								96.30 191					97.75 171	93.46 93	86.17 207	92.67 211

plane_prior4											96.52 156

plane_prior1					93.90 208

n2								0.00 363

nn								0.00 363

door-mid								84.90 348

lessismore_v0				85.08 303	85.59 318	69.28 328		90.56 331		67.68 317	90.21 274	54.21 320	95.46 276	73.88 282	62.64 320	90.50 276

test11								97.68 81

door								85.30 347

HQP5-MVS						86.39 210

BP-MVS														93.82 88

HQP4-MVS										87.57 172			97.77 166			92.72 209

HQP3-MVS								96.37 186							86.29 204

HQP2-MVS												73.34 221

NP-MVS					93.94 205	86.22 217					96.67 150

ACMMP++_ref															82.64 234

ACMMP++															83.83 223

Test By Simon												83.62 123

High-resolution multi-view benchmark