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_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

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

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

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

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

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

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

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

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

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

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	71.29 244	99.64 72	87.94 148	97.20 101	98.98 97

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

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
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

HY-MVS	88.56 7	95.29 63	94.23 70	98.48 8	97.72 91	96.41 8	94.03 291	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

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

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	61.95 298	99.34 103	94.75 75	55.72 339	97.30 171

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
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

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	72.53 291	91.81 233

CHOSEN 280x420		96.80 27	96.85 19	96.66 80	97.85 88	94.42 42	94.76 283	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

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	68.84 260	97.10 204	90.92 117	75.34 263	92.23 221

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

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

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	60.52 303	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
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

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

WR-MVS_H		86.53 228	85.49 223	89.66 253	91.04 251	83.31 260	97.53 206	98.20 32	84.95 208	79.64 252	90.90 245	78.01 178	95.33 280	76.29 256	72.81 288	90.35 278

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

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

MVS		93.92 87	92.28 108	98.83 2	95.69 160	96.82 3	96.22 253	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

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

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	72.47 292	92.25 219

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

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

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	71.85 300	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
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

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	90.05 286

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

EPMVS		92.59 127	91.59 127	95.59 120	97.22 112	90.03 135	91.78 311	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

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

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

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

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

1314		93.44 100	91.98 119	97.84 22	95.24 169	94.38 43	96.22 253	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

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

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

tfpnnormal		83.65 265	81.35 270	90.56 232	91.37 248	88.06 167	97.29 211	97.87 59	78.51 292	76.20 276	90.91 244	64.78 286	96.47 230	61.71 322	73.50 284	87.13 315

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

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	74.43 271	92.12 226

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
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

无先验							98.52 136	97.82 63	87.20 174				99.90 31	87.64 151		99.85 21

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
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

#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

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

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

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
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

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

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

1121		95.19 64	94.45 66	97.42 37	98.88 66	92.58 76	96.22 253	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

GG-mvs-BLEND				96.98 57	96.53 137	94.81 29	87.20 325	97.74 75		93.91 95	96.40 160	96.56 2	96.94 209	95.08 69	98.95 68	99.20 86

gg-mvs-nofinetune		90.00 174	87.71 190	96.89 66	96.15 149	94.69 33	85.15 331	97.74 75	68.32 330	92.97 106	60.16 344	96.10 3	96.84 211	93.89 84	98.87 69	99.14 88

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

DeepPCF-MVS	93.56 1	96.55 33	97.84 5	92.68 193	98.71 72	78.11 304	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

test_part1								97.69 79				93.96 6			99.83 12	99.90 9

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

test11								97.68 81

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

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

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

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

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.
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

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

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

agg_prior					99.54 27	92.66 72		97.64 88		97.98 26			99.61 75

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
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

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	72.47 292	92.36 215

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

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

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

CVMVSNet		90.30 166	90.91 144	88.46 275	94.32 195	73.58 317	97.61 204	97.59 96	90.16 96	88.43 162	97.10 134	76.83 184	92.86 307	82.64 198	93.54 140	98.93 104

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

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	29.59 358	87.37 75	99.87 38	95.65 58	99.43 48	99.78 30

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

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

CP-MVSNet		86.54 227	85.45 224	89.79 249	91.02 252	82.78 269	97.38 209	97.56 104	85.37 199	79.53 255	93.03 215	71.86 238	95.25 282	79.92 224	73.43 286	91.34 246

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

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

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

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

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

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

MDTV_nov1_ep13			90.47 156		96.14 150	88.55 160	91.34 314	97.51 111	89.58 104	92.24 111	90.50 266	86.99 86	97.61 179	77.64 243	92.34 150

QAPM		91.41 150	89.49 162	97.17 47	95.66 162	93.42 58	98.60 128	97.51 111	80.92 275	81.39 239	97.41 116	72.89 228	99.87 38	82.33 200	98.68 78	98.21 148

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

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

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

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

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

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

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

MTGPA								97.45 119

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

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

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
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

NR-MVSNet		87.74 205	86.00 210	92.96 186	91.46 246	90.68 120	96.65 237	97.42 125	88.02 150	73.42 291	93.68 199	77.31 181	95.83 268	84.26 180	71.82 301	92.36 215

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
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
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

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	68.00 266	99.82 51	84.52 178	98.55 82	96.11 197

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

DWT-MVSNet_test		94.36 81	93.95 80	95.62 118	96.99 120	89.47 146	96.62 238	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

tpmvs		89.16 184	87.76 188	93.35 178	97.19 113	84.75 247	90.58 321	97.36 131	81.99 262	84.56 193	89.31 287	83.98 121	98.17 144	74.85 273	90.00 187	97.12 173

PS-CasMVS		85.81 239	84.58 238	89.49 258	90.77 254	82.11 272	97.20 217	97.36 131	84.83 210	79.12 259	92.84 218	67.42 271	95.16 284	78.39 238	73.25 287	91.21 250

PatchFormer-LS_test		94.08 85	93.60 87	95.53 125	96.92 121	89.57 144	96.51 241	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

PatchmatchNet		92.05 142	91.04 135	95.06 139	96.17 148	89.04 151	91.26 315	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.
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

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

PEN-MVS		85.21 246	83.93 246	89.07 265	89.89 269	81.31 280	97.09 221	97.24 137	84.45 215	78.66 261	92.68 220	68.44 262	94.87 289	75.98 258	70.92 305	91.04 259

ab-mvs		91.05 156	89.17 167	96.69 78	95.96 153	91.72 85	92.62 303	97.23 138	85.61 194	89.74 149	93.89 195	68.55 261	99.42 95	91.09 114	87.84 199	98.92 105

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

Patchmatch-test1		90.10 171	88.61 178	94.57 151	94.95 185	88.83 153	96.26 249	97.21 140	90.06 100	90.03 144	90.68 253	66.61 277	95.83 268	77.31 244	94.36 134	99.05 92

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	62.21 297	96.76 215	89.34 135	72.26 296	92.36 215

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	71.30 304	92.23 221

cdsmvs_eth3d_5k		22.52 332	30.03 333	0.00 348	0.00 362	0.00 363	0.00 354	97.17 143	0.00 358	0.00 359	98.77 65	74.35 207	0.00 361	0.00 358	0.00 359	0.00 359

tpm2		91.77 144	91.09 134	93.82 173	94.83 188	85.56 237	92.51 304	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

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
PatchMatch-RL		91.47 148	90.54 154	94.26 159	98.20 81	86.36 212	96.94 225	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

JIA-IIPM		85.97 235	84.85 233	89.33 260	93.23 224	73.68 316	85.05 332	97.13 147	69.62 326	91.56 118	68.03 342	88.03 66	96.96 207	77.89 242	93.12 141	97.34 170

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

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

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
tpmrst		92.78 117	92.16 112	94.65 149	96.27 144	87.45 180	91.83 310	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

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
tpm cat1		88.89 188	87.27 196	93.76 174	95.79 156	85.32 238	90.76 319	97.09 152	76.14 307	85.72 186	88.59 292	82.92 140	98.04 151	76.96 248	91.43 166	97.90 160

tpmp4_e23		91.05 156	90.07 158	93.97 169	95.77 158	85.30 239	92.64 302	97.09 152	84.42 216	91.53 119	90.31 269	87.38 74	97.82 162	80.86 217	90.62 177	98.79 114

dp		90.16 170	88.83 174	94.14 162	96.38 142	86.42 208	91.57 312	97.06 155	84.76 211	88.81 159	90.19 277	84.29 119	97.43 188	75.05 270	91.35 169	98.56 129

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

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

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
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	91.93 327	72.98 293	85.70 212	93.73 204

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

CostFormer		92.89 116	92.48 105	94.12 163	94.99 184	85.89 228	92.89 301	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

UA-Net		93.30 107	92.62 102	95.34 129	96.27 144	88.53 162	95.88 267	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

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

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

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

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	71.58 242	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
PMMVS		93.62 98	93.90 83	92.79 189	96.79 132	81.40 277	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

ADS-MVSNet		88.99 186	87.30 195	94.07 164	96.21 146	87.56 177	87.15 326	96.78 168	83.01 248	89.91 146	87.27 302	78.87 170	97.01 206	74.20 278	92.27 152	97.64 162

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

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	70.54 302	90.31 269	63.50 292	98.40 138	92.25 108	95.44 127	98.60 125

test_normal		89.37 183	87.18 199	95.93 111	88.94 294	90.83 115	98.24 171	96.62 171	89.31 110	70.38 304	90.20 276	63.50 292	98.37 139	92.06 110	95.41 128	98.59 128

LS3D		90.19 169	88.72 175	94.59 150	98.97 60	86.33 213	96.90 227	96.60 172	74.96 310	84.06 199	98.74 68	75.78 188	99.83 48	74.93 271	97.57 94	97.62 165

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

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

cascas		90.93 159	89.33 166	95.76 116	95.69 160	93.03 66	98.99 83	96.59 173	80.49 277	86.79 183	94.45 185	65.23 285	98.60 137	93.52 92	92.18 155	95.66 199

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	59.09 329	97.88 88	98.72 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
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

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
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

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

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

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

VDDNet		90.08 173	88.54 183	94.69 148	94.41 194	87.68 174	98.21 176	96.40 185	76.21 306	93.33 101	97.75 105	54.93 318	98.77 122	94.71 77	90.96 170	97.61 166

HQP3-MVS								96.37 186							86.29 204

PatchT		85.44 244	83.19 248	92.22 198	93.13 226	83.00 262	83.80 339	96.37 186	70.62 320	90.55 134	79.63 333	84.81 115	94.87 289	58.18 331	91.59 164	98.79 114

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

pcd1.5k->3k		35.91 331	37.64 331	30.74 344	89.49 285	0.00 363	0.00 354	96.36 189	0.00 358	0.00 359	0.00 360	69.17 257	0.00 361	0.00 358	83.71 225	92.21 223

UnsupCasMVSNet_eth		78.90 296	76.67 298	85.58 303	82.81 328	74.94 311	91.98 309	96.31 190	84.64 212	65.84 326	87.71 297	51.33 326	92.23 323	72.89 295	56.50 338	89.56 295

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_prior5								96.30 191					97.75 171	93.46 93	86.17 207	92.67 211

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.
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	69.62 253	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
RPMNet		84.62 250	81.78 264	93.16 182	93.47 217	86.24 215	84.97 333	96.28 195	64.85 336	90.76 131	78.80 335	80.95 160	94.82 291	53.76 334	92.17 156	98.41 135

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	68.09 264	96.96 207	82.54 199	90.15 182	98.05 151

DTE-MVSNet		84.14 261	82.80 254	88.14 282	88.95 293	79.87 291	96.81 229	96.24 197	83.50 241	77.60 272	92.52 222	67.89 268	94.24 298	72.64 296	69.05 309	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	61.12 302	98.85 119	94.45 81	92.82 144	99.32 75

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

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

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	88.05 304	82.21 271	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

MS-PatchMatch		86.75 222	85.92 211	89.22 261	91.97 237	82.47 270	96.91 226	96.14 203	83.74 230	77.73 270	93.53 205	58.19 307	97.37 197	76.75 252	98.35 84	87.84 305

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	54.27 320	99.20 106	96.59 43	90.63 176	98.31 145

PCF-MVS	89.78 5	91.26 151	89.63 161	96.16 103	95.44 166	91.58 90	95.29 279	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
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

jajsoiax		87.35 208	86.51 204	89.87 245	87.75 309	81.74 274	97.03 223	95.98 207	88.47 133	80.15 247	93.80 197	61.47 299	96.36 238	89.44 133	84.47 220	91.50 241

diffmvs		92.07 138	90.77 150	95.97 109	96.41 141	91.32 100	96.46 242	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

PS-MVSNAJss		89.54 180	89.05 169	91.00 224	88.77 295	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

F-COLMAP		92.07 138	91.75 124	93.02 185	98.16 84	82.89 266	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

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	66.80 275	98.09 147	84.58 177	92.38 149	98.17 150

CMPMVS	58.40 21	80.48 289	80.11 277	81.59 318	85.10 320	59.56 337	94.14 290	95.95 212	68.54 329	60.71 331	93.31 208	55.35 317	97.87 159	83.06 194	84.85 217	87.33 311
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LPG-MVS_test		88.86 189	88.47 184	90.06 242	93.35 222	80.95 285	98.22 173	95.94 213	87.73 161	83.17 206	96.11 165	66.28 279	97.77 166	90.19 124	85.19 213	91.46 243

LGP-MVS_train				90.06 242	93.35 222	80.95 285		95.94 213	87.73 161	83.17 206	96.11 165	66.28 279	97.77 166	90.19 124	85.19 213	91.46 243

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	67.06 274	97.87 159	89.29 138	86.08 209	91.26 249

XVG-OURS-SEG-HR		90.95 158	90.66 153	91.83 204	95.18 175	81.14 283	95.92 264	95.92 216	88.40 140	90.33 141	97.85 100	70.66 247	99.38 98	92.83 103	88.83 196	94.98 200

XVG-OURS		90.83 160	90.49 155	91.86 203	95.23 170	81.25 281	95.79 272	95.92 216	88.96 122	90.02 145	98.03 99	71.60 241	99.35 102	91.06 115	87.78 200	94.98 200

tpm		89.67 178	88.95 171	91.82 205	92.54 230	81.43 276	92.95 300	95.92 216	87.81 157	90.50 135	89.44 284	84.99 111	95.65 272	83.67 190	82.71 233	98.38 139

ACMM	86.95 13	88.77 194	88.22 187	90.43 235	93.61 214	81.34 279	98.50 140	95.92 216	87.88 156	83.85 200	95.20 177	67.20 272	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
mvs_tets		87.09 217	86.22 207	89.71 250	87.87 305	81.39 278	96.73 234	95.90 220	88.19 147	79.99 248	93.61 202	59.96 305	96.31 248	89.40 134	84.34 221	91.43 245

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	67.51 270	97.10 204	85.16 171	78.41 250	92.04 230

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	75.21 264	92.53 213

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

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	62.89 294	95.29 281	77.23 245	79.71 247	91.90 232

Effi-MVS+		93.87 89	93.15 92	96.02 106	95.79 156	90.76 117	96.70 235	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

EU-MVSNet		84.19 259	84.42 241	83.52 311	88.64 298	67.37 331	96.04 261	95.76 226	85.29 200	78.44 267	93.18 212	70.67 246	91.48 330	75.79 266	75.98 259	91.70 235

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

Test4		85.71 243	82.59 260	95.07 138	84.45 322	89.84 139	97.20 217	95.73 228	89.19 114	64.59 327	87.58 298	40.59 341	96.77 214	88.95 142	95.01 130	98.60 125

testing_2		80.92 286	77.24 294	91.98 202	78.88 336	87.83 171	93.96 292	95.72 229	84.27 218	56.20 337	80.42 328	38.64 343	96.40 235	87.20 153	79.85 245	91.72 234

anonymousdsp		86.69 223	85.75 218	89.53 255	86.46 318	82.94 263	96.39 244	95.71 230	83.97 222	79.63 253	90.70 249	68.85 259	95.94 264	86.01 163	84.02 222	89.72 292

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

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

v7n		84.42 256	82.75 257	89.43 259	88.15 302	81.86 273	96.75 233	95.67 233	80.53 276	78.38 268	89.43 285	69.89 249	96.35 243	73.83 284	72.13 298	90.07 284

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	74.34 273	91.09 253

ACMP	87.39 10	88.71 196	88.24 186	90.12 241	93.91 207	81.06 284	98.50 140	95.67 233	89.43 109	80.37 244	95.55 171	65.67 282	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
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	74.33 274	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	74.33 274	91.07 256

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	74.08 280	91.08 254

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	74.08 280	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	74.05 282	91.08 254

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	74.28 277	90.32 279

XVG-ACMP-BASELINE		85.86 237	84.95 231	88.57 272	89.90 268	77.12 307	94.30 287	95.60 242	87.40 168	82.12 228	92.99 217	53.42 323	97.66 175	85.02 173	83.83 223	90.92 262

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

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	74.14 279	91.30 248

v748		83.84 264	82.31 262	88.41 277	87.65 310	79.10 294	96.66 236	95.51 245	80.09 279	77.65 271	88.53 293	69.81 250	96.23 253	75.67 267	69.25 307	89.91 289

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

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	74.97 265	90.71 271

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	90.22 274	74.60 198	96.31 248	80.92 215	71.55 302	90.69 272

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	75.73 261	91.63 237
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testpf		80.59 288	80.13 275	81.97 316	94.25 197	71.65 323	60.37 351	95.46 250	70.99 319	76.97 274	87.74 296	73.58 218	91.67 328	76.86 250	84.97 215	82.60 339

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	74.69 269	90.63 274

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	71.58 242	96.33 245	77.23 245	74.88 266	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	74.04 283	90.79 266

Effi-MVS+-dtu		89.97 175	90.68 152	87.81 288	95.15 176	71.98 322	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	74.34 313	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

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	89.86 280	74.53 202	96.39 236	78.83 235	72.32 294	90.05 286

V4		84.20 258	82.92 251	88.02 283	87.59 312	79.91 290	96.21 256	95.36 256	79.88 280	78.51 265	89.00 289	69.52 255	96.32 246	77.96 240	72.29 295	87.83 307

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	70.73 245	96.30 251	76.85 251	74.49 270	90.80 265

v52		84.19 259	82.92 251	88.01 284	87.64 311	79.92 289	96.23 251	95.32 259	79.87 281	78.51 265	89.05 288	69.50 256	96.32 246	77.95 241	72.24 297	87.79 308

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

GBi-Net		86.67 224	84.96 229	91.80 206	95.11 179	88.81 154	96.77 230	95.25 261	82.94 250	82.12 228	90.25 271	62.89 294	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	96.77 230	95.25 261	82.94 250	82.12 228	90.25 271	62.89 294	94.97 286	79.04 231	80.24 241	91.62 238

FMVSNet1		83.94 263	81.32 271	91.80 206	91.94 239	88.81 154	96.77 230	95.25 261	77.98 298	78.25 269	90.25 271	50.37 329	94.97 286	73.27 289	77.81 254	91.62 238

UnsupCasMVSNet_bld		73.85 309	70.14 311	84.99 305	79.44 334	75.73 309	88.53 324	95.24 264	70.12 325	61.94 330	74.81 338	41.41 339	93.62 300	68.65 306	51.13 345	85.62 329

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	69.60 254	96.31 248	75.59 268	74.87 267	90.72 270

v10		85.73 242	84.01 245	90.87 227	90.03 261	86.73 200	97.20 217	95.22 266	81.25 271	79.85 251	89.75 281	73.30 224	96.28 252	76.87 249	72.64 290	89.61 294

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	68.27 263	98.14 145	80.53 223	97.49 97	98.41 135

ACMH	83.09 17	84.60 251	82.61 259	90.57 231	93.18 225	82.94 263	96.27 248	94.92 268	81.01 273	72.61 299	93.61 202	56.54 311	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
IterMVS		85.81 239	84.67 237	89.22 261	93.51 216	83.67 257	96.32 247	94.80 269	85.09 204	78.69 260	90.17 278	66.57 278	93.17 303	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.
LTVRE_ROB	81.71 19	84.59 252	82.72 258	90.18 239	92.89 228	83.18 261	93.15 299	94.74 270	78.99 286	75.14 285	92.69 219	65.64 283	97.63 177	69.46 303	81.82 238	89.74 291
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
pm-mvs1		84.68 249	82.78 256	90.40 236	89.58 282	85.18 241	97.31 210	94.73 271	81.93 264	76.05 278	92.01 226	65.48 284	96.11 258	78.75 236	69.14 308	89.91 289

semantic-postprocess				89.00 266	93.46 219	82.90 265		94.70 272	85.02 206	78.62 262	90.35 267	66.63 276	93.33 302	79.38 230	77.36 257	90.76 268

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

Fast-Effi-MVS+-dtu		88.84 190	88.59 181	89.58 254	93.44 220	78.18 302	98.65 120	94.62 275	88.46 135	84.12 198	95.37 176	68.91 258	96.52 225	82.06 203	91.70 163	94.06 203

our_test_3		84.47 255	82.80 254	89.50 256	89.01 291	83.90 255	97.03 223	94.56 276	81.33 270	75.36 284	90.52 265	71.69 240	94.54 296	68.81 305	76.84 258	90.07 284

ppachtmachnet_test		83.63 266	81.57 268	89.80 248	89.01 291	85.09 244	97.13 220	94.50 277	78.84 287	76.14 277	91.00 239	69.78 251	94.61 295	63.40 318	74.36 272	89.71 293

test2356		80.96 285	81.77 265	78.52 322	81.02 330	62.33 333	98.22 173	94.49 278	79.38 284	74.56 286	90.34 268	70.65 248	85.10 341	60.83 323	86.42 203	88.14 302

YYNet1		79.64 294	77.04 296	87.43 292	87.80 307	79.98 288	96.23 251	94.44 279	73.83 315	51.83 339	87.53 300	67.96 267	92.07 326	66.00 314	67.75 314	90.23 281

MDA-MVSNet_test_wron		79.65 293	77.05 295	87.45 291	87.79 308	80.13 287	96.25 250	94.44 279	73.87 314	51.80 340	87.47 301	68.04 265	92.12 325	66.02 313	67.79 313	90.09 282

MIMVSNet		84.48 254	81.83 263	92.42 196	91.73 243	87.36 186	85.52 329	94.42 281	81.40 269	81.91 233	87.58 298	51.92 325	92.81 310	73.84 283	88.15 198	97.08 177

MVP-Stereo		86.61 226	85.83 215	88.93 267	88.70 297	83.85 256	96.07 260	94.41 282	82.15 261	75.64 282	91.96 228	67.65 269	96.45 232	77.20 247	98.72 76	86.51 318
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MSDG		88.29 201	86.37 205	94.04 166	96.90 122	86.15 220	96.52 240	94.36 283	77.89 303	79.22 258	96.95 142	69.72 252	99.59 78	73.20 290	92.58 148	96.37 196

ACMH+	83.78 15	84.21 257	82.56 261	89.15 263	93.73 213	79.16 292	96.43 243	94.28 284	81.09 272	74.00 290	94.03 189	54.58 319	97.67 174	76.10 257	78.81 249	90.63 274

Patchmatch-test		86.25 232	84.06 244	92.82 188	94.42 193	82.88 267	82.88 341	94.23 285	71.58 317	79.39 256	90.62 259	89.00 50	96.42 233	63.03 319	91.37 168	99.16 87

CR-MVSNet		88.83 191	87.38 194	93.16 182	93.47 217	86.24 215	84.97 333	94.20 286	88.92 126	90.76 131	86.88 306	84.43 117	94.82 291	70.64 302	92.17 156	98.41 135

Patchmtry		83.61 267	81.64 266	89.50 256	93.36 221	82.84 268	84.10 336	94.20 286	69.47 327	79.57 254	86.88 306	84.43 117	94.78 293	68.48 307	74.30 276	90.88 263

EG-PatchMatch MVS		79.92 291	77.59 291	86.90 295	87.06 316	77.90 306	96.20 257	94.06 288	74.61 311	66.53 325	88.76 291	40.40 342	96.20 254	67.02 310	83.66 226	86.61 316

K. test v3		81.04 284	79.77 278	84.83 306	87.41 313	70.23 327	95.60 276	93.93 289	83.70 232	67.51 321	89.35 286	55.76 313	93.58 301	76.67 253	68.03 312	90.67 273

RPSCF		85.33 245	85.55 222	84.67 308	94.63 192	62.28 334	93.73 294	93.76 290	74.38 313	85.23 190	97.06 137	64.09 288	98.31 140	80.98 213	86.08 209	93.41 208

MVS-HIRNet		79.01 295	75.13 302	90.66 230	93.82 211	81.69 275	85.16 330	93.75 291	54.54 342	74.17 289	59.15 346	57.46 309	96.58 217	63.74 317	94.38 133	93.72 205

pmmvs5		85.87 236	84.40 242	90.30 238	88.53 299	84.23 251	98.60 128	93.71 292	81.53 268	80.29 245	92.02 225	64.51 287	95.52 275	82.04 204	78.34 251	91.15 251

pmmvs6		79.90 292	77.31 293	87.67 289	84.17 324	78.13 303	95.86 269	93.68 293	67.94 331	72.67 298	89.62 283	50.98 328	95.75 270	74.80 274	66.04 315	89.14 299

OurMVSNet-221017-0		84.13 262	83.59 247	85.77 302	87.81 306	70.24 326	94.89 282	93.65 294	86.08 190	76.53 275	93.28 210	61.41 300	96.14 257	80.95 214	77.69 255	90.93 261

DP-MVS		88.75 195	86.56 203	95.34 129	98.92 64	87.45 180	97.64 203	93.52 295	70.55 321	81.49 237	97.25 124	74.43 205	99.88 35	71.14 301	94.09 136	98.67 123

ITE_SJBPF				87.93 286	92.26 233	76.44 308		93.47 296	87.67 164	79.95 249	95.49 174	56.50 312	97.38 195	75.24 269	82.33 236	89.98 288

USDC		84.74 248	82.93 250	90.16 240	91.73 243	83.54 258	95.00 281	93.30 297	88.77 129	73.19 292	93.30 209	53.62 322	97.65 176	75.88 259	81.54 239	89.30 296

v18		82.00 271	79.76 279	88.72 269	90.03 261	86.81 199	96.17 258	93.12 298	78.70 289	68.39 308	82.10 315	74.64 194	93.00 304	74.21 277	60.45 327	86.35 319

v17		81.87 276	79.61 281	88.64 271	89.91 266	86.64 204	96.01 262	93.08 299	78.54 290	68.27 310	81.96 317	74.44 204	92.95 306	74.03 280	60.22 329	86.34 320

v16		81.90 274	79.65 280	88.65 270	90.02 263	86.66 203	96.01 262	93.07 300	78.53 291	68.27 310	82.05 316	74.39 206	92.96 305	74.02 281	60.48 326	86.33 321

ADS-MVSNet2		87.62 206	86.88 201	89.86 246	96.21 146	79.14 293	87.15 326	92.99 301	83.01 248	89.91 146	87.27 302	78.87 170	92.80 311	74.20 278	92.27 152	97.64 162

Anonymous20231206		80.76 287	79.42 283	84.79 307	84.78 321	72.98 318	96.53 239	92.97 302	79.56 283	74.33 287	88.83 290	61.27 301	92.15 324	60.59 325	75.92 260	89.24 298

v15		81.62 277	79.32 284	88.52 273	89.80 273	86.56 205	95.83 271	92.96 303	78.50 293	67.88 314	81.68 319	74.22 211	92.82 309	73.46 287	59.55 330	86.18 324

V14		81.55 279	79.26 285	88.42 276	89.80 273	86.33 213	95.72 274	92.96 303	78.35 294	67.82 315	81.70 318	74.13 212	92.78 313	73.32 288	59.50 332	86.16 326

v12		81.37 282	79.05 287	88.33 280	89.68 278	86.05 225	95.48 277	92.92 305	78.08 296	67.55 320	81.58 321	73.75 216	92.75 314	73.05 292	59.37 336	86.18 324

V9		81.46 280	79.15 286	88.39 279	89.75 275	86.17 219	95.62 275	92.92 305	78.22 295	67.65 319	81.64 320	73.95 215	92.80 311	73.15 291	59.43 335	86.21 323

v13		81.30 283	78.99 289	88.25 281	89.61 280	85.87 229	95.39 278	92.90 307	77.93 302	67.45 323	81.52 322	73.66 217	92.75 314	72.91 294	59.53 331	86.14 327

LP		77.80 303	74.39 305	88.01 284	91.93 240	79.02 295	80.88 343	92.90 307	65.43 334	72.00 300	81.29 325	65.78 281	92.73 316	43.76 344	75.58 262	92.27 218

MDA-MVSNet-bldmvs		77.82 302	74.75 304	87.03 294	88.33 300	78.52 300	96.34 246	92.85 309	75.57 308	48.87 342	87.89 295	57.32 310	92.49 321	60.79 324	64.80 318	90.08 283

test20.03		78.51 299	77.48 292	81.62 317	83.07 327	71.03 324	96.11 259	92.83 310	81.66 267	69.31 306	89.68 282	57.53 308	87.29 337	58.65 330	68.47 310	86.53 317

COLMAP_ROB	82.69 18	84.54 253	82.82 253	89.70 251	96.72 134	78.85 296	95.89 265	92.83 310	71.55 318	77.54 273	95.89 168	59.40 306	99.14 112	67.26 309	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
v11		81.38 281	79.03 288	88.41 277	89.68 278	86.43 207	95.74 273	92.82 312	78.03 297	67.74 316	81.45 323	73.33 223	92.69 317	72.23 298	60.27 328	86.11 328

SixPastTwentyTwo		82.63 268	81.58 267	85.79 301	88.12 303	71.01 325	95.17 280	92.54 313	84.33 217	72.93 296	92.08 223	60.41 304	95.61 274	74.47 275	74.15 278	90.75 269

FMVSNet5		82.29 269	80.54 274	87.52 290	93.79 212	84.01 253	93.73 294	92.47 314	76.92 305	74.27 288	86.15 310	63.69 291	89.24 333	69.07 304	74.79 268	89.29 297

new-patchmatchnet		74.80 308	72.40 309	81.99 315	78.36 337	72.20 321	94.44 284	92.36 315	77.06 304	63.47 328	79.98 332	51.04 327	88.85 334	60.53 326	54.35 340	84.92 333

new_pmnet		76.02 305	73.71 306	82.95 312	83.88 325	72.85 319	91.26 315	92.26 316	70.44 322	62.60 329	81.37 324	47.64 332	92.32 322	61.85 321	72.10 299	83.68 336

AllTest		84.97 247	83.12 249	90.52 233	96.82 130	78.84 297	95.89 265	92.17 317	77.96 300	75.94 279	95.50 172	55.48 315	99.18 107	71.15 299	87.14 201	93.55 206

TestCases				90.52 233	96.82 130	78.84 297		92.17 317	77.96 300	75.94 279	95.50 172	55.48 315	99.18 107	71.15 299	87.14 201	93.55 206

pmmvs-eth3d		78.71 298	76.16 300	86.38 297	80.25 332	81.19 282	94.17 289	92.13 319	77.97 299	66.90 324	82.31 313	55.76 313	92.56 320	73.63 286	62.31 323	85.38 330

MIMVSNet1		75.92 306	73.30 307	83.81 310	81.29 329	75.57 310	92.26 307	92.05 320	73.09 316	67.48 322	86.18 309	40.87 340	87.64 336	55.78 332	70.68 306	88.21 301

ambc				79.60 320	72.76 342	56.61 343	76.20 345	92.01 321		68.25 312	80.23 331	23.34 349	94.73 294	73.78 285	60.81 325	87.48 309

LF4IMVS		81.94 273	81.17 272	84.25 309	87.23 315	68.87 330	93.35 298	91.93 322	83.35 243	75.40 283	93.00 216	49.25 331	96.65 216	78.88 234	78.11 252	87.22 314

TransMVSNet (Re)		81.97 272	79.61 281	89.08 264	89.70 277	84.01 253	97.26 212	91.85 323	78.84 287	73.07 295	91.62 232	67.17 273	95.21 283	67.50 308	59.46 334	88.02 304

Baseline_NR-MVSNet		85.83 238	84.82 234	88.87 268	88.73 296	83.34 259	98.63 123	91.66 324	80.41 278	82.44 222	91.35 235	74.63 196	95.42 278	84.13 182	71.39 303	87.84 305

testgi		82.29 269	81.00 273	86.17 299	87.24 314	74.84 312	97.39 207	91.62 325	88.63 130	75.85 281	95.42 175	46.07 334	91.55 329	66.87 312	79.94 244	92.12 226

TDRefinement		78.01 300	75.31 301	86.10 300	70.06 344	73.84 315	93.59 297	91.58 326	74.51 312	73.08 294	91.04 238	49.63 330	97.12 201	74.88 272	59.47 333	87.33 311

OpenMVS_ROB	73.86 20	77.99 301	75.06 303	86.77 296	83.81 326	77.94 305	96.38 245	91.53 327	67.54 332	68.38 309	87.13 305	43.94 335	96.08 259	55.03 333	81.83 237	86.29 322

test_0402		78.81 297	76.33 299	86.26 298	91.18 249	78.44 301	95.88 267	91.34 328	68.55 328	70.51 303	89.91 279	52.65 324	94.99 285	47.14 339	79.78 246	85.34 332

MTMP								91.09 329

DeepMVS_CX				76.08 324	90.74 255	51.65 347		90.84 330	86.47 187	57.89 335	87.98 294	35.88 345	92.60 318	65.77 315	65.06 317	83.97 335

test1235678		71.07 312	69.53 314	75.71 325	71.87 343	55.27 345	94.32 285	90.76 331	70.23 323	57.61 336	79.06 334	43.13 336	83.72 343	50.48 336	68.30 311	88.14 302

lessismore_v0				85.08 304	85.59 319	69.28 329		90.56 332		67.68 318	90.21 275	54.21 321	95.46 276	73.88 282	62.64 321	90.50 276

testus		77.11 304	76.95 297	77.58 323	80.02 333	58.93 339	97.78 197	90.48 333	79.68 282	72.84 297	90.61 261	37.72 344	86.57 340	60.28 327	83.18 229	87.23 313

Gipuma		54.77 322	52.22 323	62.40 335	86.50 317	59.37 338	50.20 352	90.35 334	36.52 348	41.20 346	49.49 350	18.33 353	81.29 346	32.10 350	65.34 316	46.54 352
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
1111		72.28 311	71.36 310	75.02 326	73.04 340	57.38 341	92.30 305	90.22 335	62.27 338	59.46 332	80.36 329	76.23 185	87.07 338	44.29 342	64.08 319	80.59 340

.test1245		61.50 317	64.44 316	52.65 341	73.04 340	57.38 341	92.30 305	90.22 335	62.27 338	59.46 332	80.36 329	76.23 185	87.07 338	44.29 342	1.80 356	13.50 356

TinyColmap		80.42 290	77.94 290	87.85 287	92.09 236	78.58 299	93.74 293	89.94 337	74.99 309	69.77 305	91.78 230	46.09 333	97.58 180	65.17 316	77.89 253	87.38 310

FPMVS		61.57 316	60.32 318	65.34 333	60.14 350	42.44 353	91.02 317	89.72 338	44.15 345	42.63 345	80.93 326	19.02 351	80.59 348	42.50 345	72.76 289	73.00 344

LCM-MVSNet		60.07 319	56.37 320	71.18 327	54.81 354	48.67 349	82.17 342	89.48 339	37.95 346	49.13 341	69.12 339	13.75 358	81.76 345	59.28 328	51.63 344	83.10 338

pmmvs3		72.86 310	69.76 313	82.17 313	73.86 339	74.19 314	94.20 288	89.01 340	64.23 337	67.72 317	80.91 327	41.48 338	88.65 335	62.40 320	54.02 341	83.68 336

Anonymous20231211		67.10 314	63.29 317	78.54 321	75.68 338	60.00 336	92.05 308	88.86 341	49.84 343	59.35 334	78.48 336	26.15 348	90.76 331	45.96 341	53.24 342	84.88 334

test12356		66.36 315	65.12 315	70.08 331	66.92 345	50.46 348	89.96 322	88.58 342	66.00 333	53.38 338	78.13 337	32.89 346	82.87 344	48.36 338	61.87 324	76.92 341

LCM-MVSNet-Re		88.59 197	88.61 178	88.51 274	95.53 164	72.68 320	96.85 228	88.43 343	88.45 136	73.14 293	90.63 258	75.82 187	94.38 297	92.95 100	95.71 124	98.48 133

Patchmatch-RL test		81.90 274	80.13 275	87.23 293	80.71 331	70.12 328	84.07 337	88.19 344	83.16 246	70.57 301	82.18 314	87.18 81	92.59 319	82.28 201	62.78 320	98.98 97

testmv		60.41 318	57.98 319	67.69 332	58.16 353	47.14 350	89.09 323	86.74 345	61.52 341	44.30 344	68.44 340	20.98 350	79.92 349	40.94 346	51.67 343	76.01 342

DSMNet-mixed		81.60 278	81.43 269	82.10 314	84.36 323	60.79 335	93.63 296	86.74 345	79.00 285	79.32 257	87.15 304	63.87 290	89.78 332	66.89 311	91.92 158	95.73 198

PM-MVS		74.88 307	72.85 308	80.98 319	78.98 335	64.75 332	90.81 318	85.77 347	80.95 274	68.23 313	82.81 312	29.08 347	92.84 308	76.54 255	62.46 322	85.36 331

door								85.30 348

door-mid								84.90 349

no-one		56.69 321	51.89 324	71.08 329	59.35 352	58.65 340	83.78 340	84.81 350	61.73 340	36.46 348	56.52 348	18.15 354	84.78 342	47.03 340	19.19 350	69.81 346

PMMVS2		58.97 320	55.07 321	70.69 330	62.72 346	55.37 344	85.97 328	80.52 351	49.48 344	45.94 343	68.31 341	15.73 356	80.78 347	49.79 337	37.12 346	75.91 343

ANet_high		50.71 324	46.17 325	64.33 334	44.27 357	52.30 346	76.13 346	78.73 352	64.95 335	27.37 351	55.23 349	14.61 357	67.74 353	36.01 349	18.23 352	72.95 345

PMVS	41.42 23	45.67 326	42.50 327	55.17 339	34.28 358	32.37 357	66.24 349	78.71 353	30.72 350	22.04 354	59.59 345	4.59 360	77.85 350	27.49 351	58.84 337	55.29 350
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		53.66 323	52.86 322	56.05 338	32.75 359	41.97 355	73.42 347	76.12 354	21.91 354	39.68 347	96.39 162	42.59 337	65.10 354	78.00 239	14.92 354	61.08 349

wuykxyi23d		43.53 327	37.95 330	60.27 336	45.36 356	44.79 351	68.27 348	74.26 355	33.48 349	18.21 356	40.16 357	3.64 361	71.01 351	38.85 347	19.31 349	65.02 347

PNet_i23d		48.05 325	44.98 326	57.28 337	60.15 348	42.39 354	80.85 344	73.14 356	36.78 347	27.46 350	56.66 347	6.38 359	68.34 352	36.65 348	26.72 348	61.10 348

MVE	44.00 22	41.70 328	37.64 331	53.90 340	49.46 355	43.37 352	65.09 350	66.66 357	26.19 353	25.77 353	48.53 351	3.58 363	63.35 355	26.15 352	27.28 347	54.97 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		41.02 329	40.93 328	41.29 342	61.97 347	33.83 356	84.00 338	65.17 358	27.17 351	27.56 349	46.72 352	17.63 355	60.41 356	19.32 353	18.82 351	29.61 353

EMVS		39.96 330	39.88 329	40.18 343	59.57 351	32.12 358	84.79 335	64.57 359	26.27 352	26.14 352	44.18 355	18.73 352	59.29 357	17.03 354	17.67 353	29.12 354

N_pmnet		70.19 313	69.87 312	71.12 328	88.24 301	30.63 359	95.85 270	28.70 360	70.18 324	68.73 307	86.55 308	64.04 289	93.81 299	53.12 335	73.46 285	88.94 300

wuyk23d		16.71 334	16.73 336	16.65 345	60.15 348	25.22 360	41.24 353	5.17 361	6.56 355	5.48 358	3.61 359	3.64 361	22.72 358	15.20 355	9.52 355	1.99 358

testmvs		18.81 333	23.05 334	6.10 347	4.48 360	2.29 362	97.78 197	3.00 362	3.27 356	18.60 355	62.71 343	1.53 365	2.49 360	14.26 356	1.80 356	13.50 356

test123		16.58 335	19.47 335	7.91 346	3.59 361	5.37 361	94.32 285	1.39 363	2.49 357	13.98 357	44.60 354	2.91 364	2.65 359	11.35 357	0.57 358	15.70 355

pcd_1.5k_mvsjas		6.87 337	9.16 338	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	82.48 146	0.00 361	0.00 358	0.00 359	0.00 359

sosnet-low-res		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

sosnet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

uncertanet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

Regformer		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

n2								0.00 364

nn								0.00 364

ab-mvs-re		8.21 336	10.94 337	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	98.50 84	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

uanet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

GSMVS																98.84 109

test_part3							99.43 33		92.81 44		99.48 4		99.97 14	99.52 1

test_part2					99.54 27	95.42 14				98.13 17

sam_mvs1												88.39 59				98.84 109

sam_mvs												87.08 82

test_post1							90.74 320				41.37 356	85.38 110	96.36 238	83.16 192

test_post											46.00 353	87.37 75	97.11 202

patchmatchnet-post											84.86 311	88.73 53	96.81 213

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

test9_res														98.60 11	99.87 5	99.90 9

agg_prior2														97.84 28	99.87 5	99.91 8

test_prior4						92.00 81	99.41 38

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

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

新几何2							98.26 169

原ACMM2							98.69 113

testdata2													99.88 35	84.16 181

segment_acmp												90.56 35

testdata1							97.89 193		92.43 50

plane_prior7					93.84 209	85.73 233

plane_prior6					93.92 206	86.02 226						72.92 226

plane_prior4											96.52 156

plane_prior3						85.91 227			93.65 30	86.99 178

plane_prior2							99.02 78		93.38 35

plane_prior1					93.90 208

plane_prior						86.07 223	99.14 66		93.81 28						86.26 206

HQP5-MVS						86.39 210

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

BP-MVS														93.82 88

HQP4-MVS										87.57 172			97.77 166			92.72 209

HQP2-MVS												73.34 221

NP-MVS					93.94 205	86.22 217					96.67 150

MDTV_nov1_ep13_2view						91.17 104	91.38 313		87.45 167	93.08 103		86.67 89		87.02 156		98.95 103

ACMMP++_ref															82.64 234

ACMMP++															83.83 223

Test By Simon												83.62 123

High-resolution multi-view benchmark