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.

EPNet		91.79 59	91.02 66	94.10 44	90.10 274	85.25 52	96.03 34	92.05 248	92.83 1	87.39 116	95.78 73	79.39 93	99.01 53	88.13 79	97.48 57	98.05 45
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
NCCC		94.81 8	94.69 9	95.17 6	97.83 30	87.46 9	95.66 49	96.93 39	92.34 2	93.94 18	96.58 43	87.74 12	99.44 18	92.83 20	98.40 37	98.62 5

CNVR-MVS		95.40 2	95.37 3	95.50 3	98.11 23	88.51 3	95.29 61	96.96 36	92.09 3	95.32 7	97.08 23	89.49 4	99.33 24	95.10 2	98.85 6	98.66 4

UA-Net		92.83 51	92.54 51	93.68 55	96.10 78	84.71 57	95.66 49	96.39 76	91.92 4	93.22 30	96.49 47	83.16 53	98.87 65	84.47 119	95.47 86	97.45 71

CANet		93.54 36	93.20 39	94.55 31	95.65 92	85.73 48	94.94 84	96.69 59	91.89 5	90.69 75	95.88 70	81.99 69	99.54 8	93.14 18	97.95 49	98.39 19

Regformer-2		94.33 18	94.22 16	94.68 26	95.54 95	86.75 18	94.57 108	96.70 57	91.84 6	94.41 10	96.56 45	87.19 18	99.13 38	93.50 11	97.65 55	98.16 36

MVS_0304		93.25 45	92.62 49	95.14 7	95.72 90	87.58 7	94.71 101	96.59 66	91.78 7	91.46 67	96.18 61	75.45 144	99.55 5	93.53 10	98.19 42	98.28 26

HPM-MVS++		95.14 5	94.91 7	95.83 1	98.25 19	89.65 1	95.92 38	96.96 36	91.75 8	94.02 17	96.83 30	88.12 9	99.55 5	93.41 15	98.94 3	98.28 26

HSP-MVS		95.30 3	95.48 2	94.76 23	98.49 8	86.52 26	96.91 15	96.73 53	91.73 9	96.10 3	96.69 36	89.90 1	99.30 27	94.70 3	98.04 47	98.45 16

Regformer-1		94.22 22	94.13 21	94.51 33	95.54 95	86.36 31	94.57 108	96.44 71	91.69 10	94.32 12	96.56 45	87.05 20	99.03 48	93.35 16	97.65 55	98.15 37

Regformer-4		93.91 29	93.81 27	94.19 43	95.36 99	85.47 49	94.68 102	96.41 74	91.60 11	93.75 20	96.71 34	85.95 30	99.10 41	93.21 17	96.65 70	98.01 49

SteuartSystems-ACMMP		95.20 4	95.32 5	94.85 15	96.99 53	86.33 32	97.33 3	97.30 16	91.38 12	95.39 6	97.46 7	88.98 8	99.40 19	94.12 7	98.89 5	98.82 2
Skip Steuart: Steuart Systems R&D Blog.
Regformer-3		93.68 33	93.64 33	93.81 52	95.36 99	84.61 58	94.68 102	95.83 112	91.27 13	93.60 24	96.71 34	85.75 32	98.86 68	92.87 19	96.65 70	97.96 50

MPTG		94.47 11	94.30 13	95.00 8	98.42 12	86.95 10	95.06 80	96.97 33	91.07 14	93.14 32	97.56 4	84.30 47	99.56 1	93.43 13	98.75 14	98.47 12

MTAPA		94.42 16	94.22 16	95.00 8	98.42 12	86.95 10	94.36 128	96.97 33	91.07 14	93.14 32	97.56 4	84.30 47	99.56 1	93.43 13	98.75 14	98.47 12

EI-MVSNet-Vis-set		93.01 50	92.92 45	93.29 58	95.01 109	83.51 88	94.48 111	95.77 115	90.87 16	92.52 47	96.67 38	84.50 46	99.00 56	91.99 36	94.44 105	97.36 72

3Dnovator+	87.14 4	92.42 55	91.37 59	95.55 2	95.63 93	88.73 2	97.07 8	96.77 51	90.84 17	84.02 198	96.62 41	75.95 133	99.34 21	87.77 83	97.68 53	98.59 7

HQP_MVS		90.60 82	90.19 77	91.82 115	94.70 123	82.73 110	95.85 40	96.22 85	90.81 18	86.91 123	94.86 95	74.23 156	98.12 106	88.15 77	89.99 157	94.63 162

plane_prior2							95.85 40		90.81 18

DELS-MVS		93.43 39	93.25 37	93.97 45	95.42 98	85.04 53	93.06 199	97.13 24	90.74 20	91.84 60	95.09 90	86.32 26	99.21 30	91.22 50	98.45 36	97.65 63
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-UG-set		92.74 53	92.62 49	93.12 64	94.86 117	83.20 94	94.40 118	95.74 118	90.71 21	92.05 57	96.60 42	84.00 49	98.99 57	91.55 46	93.63 114	97.17 79

XVS		94.45 12	94.32 12	94.85 15	98.54 5	86.60 24	96.93 12	97.19 21	90.66 22	92.85 34	97.16 21	85.02 41	99.49 14	91.99 36	98.56 33	98.47 12

X-MVStestdata		88.31 135	86.13 178	94.85 15	98.54 5	86.60 24	96.93 12	97.19 21	90.66 22	92.85 34	23.41 336	85.02 41	99.49 14	91.99 36	98.56 33	98.47 12

SD-MVS		94.96 6	95.33 4	93.88 48	97.25 50	86.69 19	96.19 29	97.11 27	90.42 24	96.95 1	97.27 11	89.53 3	96.91 209	94.38 5	98.85 6	98.03 47

plane_prior3						82.75 107			90.26 25	86.91 123

DeepPCF-MVS	89.96 1	94.20 24	94.77 8	92.49 87	96.52 64	80.00 169	94.00 152	97.08 28	90.05 26	95.65 5	97.29 10	89.66 2	98.97 59	93.95 8	98.71 17	98.50 9

MSLP-MVS++		93.72 32	94.08 22	92.65 81	97.31 44	83.43 89	95.79 42	97.33 13	90.03 27	93.58 25	96.96 26	84.87 43	97.76 133	92.19 31	98.66 26	96.76 92

canonicalmvs		93.27 43	92.75 48	94.85 15	95.70 91	87.66 5	96.33 25	96.41 74	90.00 28	94.09 15	94.60 104	82.33 60	98.62 82	92.40 26	92.86 132	98.27 29

Vis-MVSNet		91.75 61	91.23 62	93.29 58	95.32 102	83.78 80	96.14 30	95.98 100	89.89 29	90.45 77	96.58 43	75.09 148	98.31 99	84.75 116	96.90 64	97.78 62
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TranMVSNet+NR-MVSNet		88.84 124	87.95 127	91.49 124	92.68 186	83.01 101	94.92 86	96.31 79	89.88 30	85.53 157	93.85 131	76.63 118	96.96 206	81.91 156	79.87 278	94.50 173

UniMVSNet_NR-MVSNet		89.92 95	89.29 94	91.81 117	93.39 167	83.72 81	94.43 116	97.12 25	89.80 31	86.46 130	93.32 140	83.16 53	97.23 187	84.92 112	81.02 260	94.49 175

alignmvs		93.08 49	92.50 52	94.81 20	95.62 94	87.61 6	95.99 35	96.07 95	89.77 32	94.12 14	94.87 94	80.56 78	98.66 78	92.42 25	93.10 127	98.15 37

TSAR-MVS + GP.		93.66 34	93.41 35	94.41 37	96.59 61	86.78 16	94.40 118	93.93 215	89.77 32	94.21 13	95.59 79	87.35 16	98.61 83	92.72 21	96.15 78	97.83 59

IS-MVSNet		91.43 66	91.09 65	92.46 88	95.87 87	81.38 135	96.95 9	93.69 220	89.72 34	89.50 87	95.98 66	78.57 100	97.77 132	83.02 137	96.50 74	98.22 33

plane_prior						82.73 110	95.21 71		89.66 35						89.88 160

DU-MVS		89.34 113	88.50 111	91.85 113	93.04 178	83.72 81	94.47 113	96.59 66	89.50 36	86.46 130	93.29 143	77.25 111	97.23 187	84.92 112	81.02 260	94.59 166

CANet_DTU		90.26 87	89.41 91	92.81 76	93.46 166	83.01 101	93.48 179	94.47 192	89.43 37	87.76 111	94.23 115	70.54 209	99.03 48	84.97 111	96.39 76	96.38 100

DeepC-MVS_fast	89.43 2	94.04 25	93.79 28	94.80 21	97.48 39	86.78 16	95.65 51	96.89 41	89.40 38	92.81 37	96.97 25	85.37 36	99.24 29	90.87 56	98.69 19	98.38 20
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UGNet		89.95 93	88.95 102	92.95 72	94.51 130	83.31 92	95.70 46	95.23 162	89.37 39	87.58 113	93.94 124	64.00 263	98.78 75	83.92 129	96.31 77	96.74 94
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		90.27 86	90.18 78	90.53 152	93.71 160	79.85 173	95.77 43	97.59 2	89.31 40	86.27 136	94.67 101	81.93 70	97.01 202	84.26 124	88.09 189	94.71 161

UniMVSNet (Re)		89.80 97	89.07 99	92.01 103	93.60 163	84.52 61	94.78 95	97.47 5	89.26 41	86.44 133	92.32 177	82.10 65	97.39 173	84.81 115	80.84 264	94.12 185

3Dnovator	86.66 5	91.73 62	90.82 70	94.44 34	94.59 127	86.37 30	97.18 6	97.02 30	89.20 42	84.31 194	96.66 39	73.74 167	99.17 33	86.74 98	97.96 48	97.79 61

VNet		92.24 56	91.91 55	93.24 60	96.59 61	83.43 89	94.84 91	96.44 71	89.19 43	94.08 16	95.90 69	77.85 110	98.17 103	88.90 70	93.38 121	98.13 39

FIs		90.51 83	90.35 74	90.99 143	93.99 150	80.98 146	95.73 44	97.54 3	89.15 44	86.72 127	94.68 100	81.83 71	97.24 185	85.18 109	88.31 186	94.76 160

NR-MVSNet		88.58 130	87.47 134	91.93 109	93.04 178	84.16 74	94.77 96	96.25 83	89.05 45	80.04 253	93.29 143	79.02 94	97.05 200	81.71 160	80.05 273	94.59 166

MP-MVS		94.25 20	94.07 23	94.77 22	98.47 9	86.31 34	96.71 20	96.98 32	89.04 46	91.98 58	97.19 18	85.43 35	99.56 1	92.06 35	98.79 9	98.44 17

APDe-MVS		95.46 1	95.64 1	94.91 11	98.26 18	86.29 36	97.46 2	97.40 8	89.03 47	96.20 2	98.10 1	89.39 5	99.34 21	95.88 1	99.03 1	99.10 1

DeepC-MVS	88.79 3	93.31 41	92.99 43	94.26 41	96.07 80	85.83 46	94.89 87	96.99 31	89.02 48	89.56 85	97.37 8	82.51 58	99.38 20	92.20 30	98.30 39	97.57 67
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OPM-MVS		90.12 88	89.56 87	91.82 115	93.14 174	83.90 77	94.16 135	95.74 118	88.96 49	87.86 102	95.43 81	72.48 184	97.91 128	88.10 80	90.18 156	93.65 213

HQP-NCC					94.17 140		94.39 120		88.81 50	85.43 166

ACMP_Plane					94.17 140		94.39 120		88.81 50	85.43 166

HQP-MVS		89.80 97	89.28 95	91.34 128	94.17 140	81.56 127	94.39 120	96.04 98	88.81 50	85.43 166	93.97 123	73.83 165	97.96 124	87.11 95	89.77 162	94.50 173

MVS_111021_HR		93.45 37	93.31 36	93.84 49	96.99 53	84.84 54	93.24 192	97.24 18	88.76 53	91.60 66	95.85 71	86.07 29	98.66 78	91.91 40	98.16 43	98.03 47

mPP-MVS		93.99 27	93.78 29	94.63 28	98.50 7	85.90 45	96.87 16	96.91 40	88.70 54	91.83 62	97.17 20	83.96 50	99.55 5	91.44 49	98.64 29	98.43 18

VPNet		88.20 138	87.47 134	90.39 163	93.56 164	79.46 183	94.04 148	95.54 132	88.67 55	86.96 121	94.58 105	69.33 221	97.15 191	84.05 128	80.53 268	94.56 169

HFP-MVS		94.52 10	94.40 11	94.86 13	98.61 3	86.81 14	96.94 10	97.34 10	88.63 56	93.65 21	97.21 16	86.10 27	99.49 14	92.35 27	98.77 12	98.30 24

ACMMPR		94.43 14	94.28 14	94.91 11	98.63 2	86.69 19	96.94 10	97.32 15	88.63 56	93.53 28	97.26 13	85.04 40	99.54 8	92.35 27	98.78 11	98.50 9

region2R		94.43 14	94.27 15	94.92 10	98.65 1	86.67 21	96.92 14	97.23 20	88.60 58	93.58 25	97.27 11	85.22 37	99.54 8	92.21 29	98.74 16	98.56 8

WR-MVS		88.38 132	87.67 131	90.52 158	93.30 170	80.18 162	93.26 190	95.96 102	88.57 59	85.47 162	92.81 164	76.12 122	96.91 209	81.24 163	82.29 238	94.47 176

CP-MVS		94.34 17	94.21 18	94.74 25	98.39 14	86.64 23	97.60 1	97.24 18	88.53 60	92.73 41	97.23 14	85.20 38	99.32 25	92.15 32	98.83 8	98.25 32

CP-MVSNet		87.63 161	87.26 140	88.74 221	93.12 175	76.59 250	95.29 61	96.58 68	88.43 61	83.49 211	92.98 158	75.28 145	95.83 251	78.97 204	81.15 257	93.79 203

VDD-MVS		90.74 76	89.92 84	93.20 61	96.27 69	83.02 100	95.73 44	93.86 216	88.42 62	92.53 46	96.84 29	62.09 270	98.64 80	90.95 55	92.62 134	97.93 54

ACMMP		93.24 46	92.88 47	94.30 40	98.09 25	85.33 51	96.86 17	97.45 7	88.33 63	90.15 81	97.03 24	81.44 72	99.51 12	90.85 57	95.74 81	98.04 46
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
nrg030		91.08 73	90.39 73	93.17 63	93.07 176	86.91 12	96.41 24	96.26 81	88.30 64	88.37 98	94.85 97	82.19 64	97.64 140	91.09 51	82.95 232	94.96 146

ACMMP_Plus		94.74 9	94.56 10	95.28 4	98.02 28	87.70 4	95.68 47	97.34 10	88.28 65	95.30 8	97.67 3	85.90 31	99.54 8	93.91 9	98.95 2	98.60 6

PS-CasMVS		87.32 174	86.88 152	88.63 224	92.99 181	76.33 253	95.33 56	96.61 65	88.22 66	83.30 214	93.07 152	73.03 176	95.79 254	78.36 209	81.00 262	93.75 209

MVS_111021_LR		92.47 54	92.29 54	92.98 71	95.99 83	84.43 69	93.08 197	96.09 93	88.20 67	91.12 72	95.72 76	81.33 74	97.76 133	91.74 44	97.37 59	96.75 93

TSAR-MVS + MP.		94.85 7	94.94 6	94.58 30	98.25 19	86.33 32	96.11 31	96.62 64	88.14 68	96.10 3	96.96 26	89.09 7	98.94 63	94.48 4	98.68 22	98.48 11

PEN-MVS		86.80 187	86.27 176	88.40 237	92.32 191	75.71 257	95.18 72	96.38 77	87.97 69	82.82 218	93.15 148	73.39 172	95.92 247	76.15 231	79.03 281	93.59 220

testdata1							92.15 227		87.94 70

VPA-MVSNet		89.62 99	88.96 101	91.60 122	93.86 154	82.89 105	95.46 55	97.33 13	87.91 71	88.43 97	93.31 141	74.17 159	97.40 170	87.32 91	82.86 234	94.52 171

WR-MVS_H		87.80 154	87.37 136	89.10 216	93.23 172	78.12 228	95.61 52	97.30 16	87.90 72	83.72 204	92.01 190	79.65 92	96.01 244	76.36 227	80.54 267	93.16 234

CLD-MVS		89.47 105	88.90 104	91.18 133	94.22 139	82.07 122	92.13 228	96.09 93	87.90 72	85.37 172	92.45 172	74.38 154	97.56 143	87.15 93	90.43 152	93.93 193
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
abl_6		93.18 48	93.05 41	93.57 57	97.52 36	84.27 72	95.53 54	96.67 60	87.85 74	93.20 31	97.22 15	80.35 79	99.18 32	91.91 40	97.21 60	97.26 73

MG-MVS		91.77 60	91.70 57	92.00 105	97.08 52	80.03 168	93.60 176	95.18 165	87.85 74	90.89 74	96.47 48	82.06 67	98.36 93	85.07 110	97.04 63	97.62 64

LCM-MVSNet-Re		88.30 136	88.32 118	88.27 240	94.71 122	72.41 282	93.15 193	90.98 277	87.77 76	79.25 259	91.96 191	78.35 103	95.75 255	83.04 136	95.62 82	96.65 95

Effi-MVS+-dtu		88.65 128	88.35 115	89.54 203	93.33 168	76.39 251	94.47 113	94.36 195	87.70 77	85.43 166	89.56 250	73.45 170	97.26 183	85.57 107	91.28 141	94.97 143

mvs-test1		89.45 106	89.14 97	90.38 165	93.33 168	77.63 242	94.95 83	94.36 195	87.70 77	87.10 120	92.81 164	73.45 170	98.03 121	85.57 107	93.04 128	95.48 130

test_prior3		93.60 35	93.53 34	93.82 50	97.29 46	84.49 62	94.12 136	96.88 42	87.67 79	92.63 43	96.39 50	86.62 23	98.87 65	91.50 47	98.67 24	98.11 41

test_prior2							94.12 136		87.67 79	92.63 43	96.39 50	86.62 23		91.50 47	98.67 24

Vis-MVSNet (Re-imp)		89.59 101	89.44 90	90.03 186	95.74 89	75.85 256	95.61 52	90.80 282	87.66 81	87.83 108	95.40 82	76.79 115	96.46 227	78.37 208	96.73 67	97.80 60

#test#		94.32 19	94.14 20	94.86 13	98.61 3	86.81 14	96.43 23	97.34 10	87.51 82	93.65 21	97.21 16	86.10 27	99.49 14	91.68 45	98.77 12	98.30 24

PGM-MVS		93.96 28	93.72 31	94.68 26	98.43 11	86.22 37	95.30 59	97.78 1	87.45 83	93.26 29	97.33 9	84.62 45	99.51 12	90.75 58	98.57 32	98.32 23

DTE-MVSNet		86.11 200	85.48 194	87.98 247	91.65 207	74.92 260	94.93 85	95.75 117	87.36 84	82.26 223	93.04 153	72.85 177	95.82 252	74.04 247	77.46 286	93.20 232

MCST-MVS		94.45 12	94.20 19	95.19 5	98.46 10	87.50 8	95.00 81	97.12 25	87.13 85	92.51 48	96.30 52	89.24 6	99.34 21	93.46 12	98.62 30	98.73 3

Effi-MVS+		91.59 65	91.11 63	93.01 70	94.35 138	83.39 91	94.60 106	95.10 167	87.10 86	90.57 76	93.10 151	81.43 73	98.07 118	89.29 67	94.48 102	97.59 66

view600		87.62 162	86.65 163	90.53 152	96.19 71	78.52 214	95.29 61	91.09 269	87.08 87	87.84 104	93.03 154	68.86 228	98.11 108	69.44 272	91.02 146	94.96 146

view800		87.62 162	86.65 163	90.53 152	96.19 71	78.52 214	95.29 61	91.09 269	87.08 87	87.84 104	93.03 154	68.86 228	98.11 108	69.44 272	91.02 146	94.96 146

conf0.05thres1000		87.62 162	86.65 163	90.53 152	96.19 71	78.52 214	95.29 61	91.09 269	87.08 87	87.84 104	93.03 154	68.86 228	98.11 108	69.44 272	91.02 146	94.96 146

tfpn		87.62 162	86.65 163	90.53 152	96.19 71	78.52 214	95.29 61	91.09 269	87.08 87	87.84 104	93.03 154	68.86 228	98.11 108	69.44 272	91.02 146	94.96 146

thres600view7		87.65 158	86.67 162	90.59 149	96.08 79	78.72 210	94.88 89	91.58 260	87.06 91	88.08 100	92.30 178	68.91 227	98.10 112	70.05 270	91.10 142	94.96 146

APD-MVS_3200maxsize		93.78 31	93.77 30	93.80 53	97.92 29	84.19 73	96.30 26	96.87 44	86.96 92	93.92 19	97.47 6	83.88 51	98.96 62	92.71 22	97.87 50	98.26 31

OMC-MVS		91.23 69	90.62 72	93.08 66	96.27 69	84.07 75	93.52 178	95.93 103	86.95 93	89.51 86	96.13 63	78.50 101	98.35 95	85.84 105	92.90 131	96.83 91

thres400		87.62 162	86.64 167	90.57 150	95.99 83	78.64 212	94.58 107	91.98 252	86.94 94	88.09 99	91.77 195	69.18 226	98.10 112	70.13 269	91.10 142	94.96 146

HPM-MVS		94.02 26	93.88 26	94.43 36	98.39 14	85.78 47	97.25 5	97.07 29	86.90 95	92.62 45	96.80 33	84.85 44	99.17 33	92.43 24	98.65 28	98.33 22

LFMVS		90.08 89	89.13 98	92.95 72	96.71 58	82.32 119	96.08 32	89.91 298	86.79 96	92.15 56	96.81 31	62.60 267	98.34 96	87.18 92	93.90 110	98.19 34

LPG-MVS_test		89.45 106	88.90 104	91.12 134	94.47 131	81.49 130	95.30 59	96.14 89	86.73 97	85.45 163	95.16 87	69.89 214	98.10 112	87.70 84	89.23 171	93.77 207

LGP-MVS_train				91.12 134	94.47 131	81.49 130		96.14 89	86.73 97	85.45 163	95.16 87	69.89 214	98.10 112	87.70 84	89.23 171	93.77 207

EPNet_dtu		86.49 196	85.94 185	88.14 245	90.24 272	72.82 274	94.11 138	92.20 244	86.66 99	79.42 258	92.36 176	73.52 168	95.81 253	71.26 260	93.66 113	95.80 122
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMP	84.23 8	89.01 122	88.35 115	90.99 143	94.73 120	81.27 136	95.07 78	95.89 109	86.48 100	83.67 206	94.30 110	69.33 221	97.99 123	87.10 97	88.55 178	93.72 211
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVS_Test		91.31 68	91.11 63	91.93 109	94.37 135	80.14 164	93.46 181	95.80 113	86.46 101	91.35 69	93.77 133	82.21 63	98.09 116	87.57 86	94.95 93	97.55 69

thres200		87.21 180	86.24 177	90.12 177	95.36 99	78.53 213	93.26 190	92.10 245	86.42 102	88.00 101	91.11 222	69.24 225	98.00 122	69.58 271	91.04 145	93.83 202

PAPM_NR		91.22 70	90.78 71	92.52 86	97.60 33	81.46 132	94.37 124	96.24 84	86.39 103	87.41 114	94.80 99	82.06 67	98.48 89	82.80 141	95.37 88	97.61 65

PS-MVSNAJ		91.18 71	90.92 67	91.96 107	95.26 105	82.60 116	92.09 230	95.70 120	86.27 104	91.84 60	92.46 171	79.70 88	98.99 57	89.08 68	95.86 80	94.29 179

MP-MVS-pluss		94.21 23	94.00 25	94.85 15	98.17 22	86.65 22	94.82 92	97.17 23	86.26 105	92.83 36	97.87 2	85.57 34	99.56 1	94.37 6	98.92 4	98.34 21
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PS-MVSNAJss		89.97 92	89.62 86	91.02 141	91.90 197	80.85 151	95.26 68	95.98 100	86.26 105	86.21 137	94.29 111	79.70 88	97.65 138	88.87 71	88.10 187	94.57 168

EPP-MVSNet		91.70 63	91.56 58	92.13 102	95.88 85	80.50 160	97.33 3	95.25 158	86.15 107	89.76 84	95.60 78	83.42 52	98.32 98	87.37 90	93.25 124	97.56 68

XVG-OURS		89.40 111	88.70 107	91.52 123	94.06 143	81.46 132	91.27 245	96.07 95	86.14 108	88.89 93	95.77 74	68.73 233	97.26 183	87.39 89	89.96 159	95.83 120

xiu_mvs_v2_base		91.13 72	90.89 69	91.86 112	94.97 112	82.42 117	92.24 224	95.64 126	86.11 109	91.74 65	93.14 149	79.67 91	98.89 64	89.06 69	95.46 87	94.28 180

Fast-Effi-MVS+-dtu		87.44 171	86.72 160	89.63 201	92.04 196	77.68 241	94.03 149	93.94 214	85.81 110	82.42 221	91.32 212	70.33 211	97.06 199	80.33 181	90.23 155	94.14 184

XVG-OURS-SEG-HR		89.95 93	89.45 89	91.47 125	94.00 149	81.21 140	91.87 232	96.06 97	85.78 111	88.55 95	95.73 75	74.67 152	97.27 181	88.71 72	89.64 164	95.91 115

HPM-MVS_fast		93.40 40	93.22 38	93.94 47	98.36 16	84.83 55	97.15 7	96.80 48	85.77 112	92.47 49	97.13 22	82.38 59	99.07 42	90.51 60	98.40 37	97.92 55

EI-MVSNet		89.10 116	88.86 106	89.80 196	91.84 199	78.30 224	93.70 171	95.01 170	85.73 113	87.15 118	95.28 83	79.87 85	97.21 189	83.81 131	87.36 195	93.88 197

IterMVS-LS		88.36 134	87.91 129	89.70 199	93.80 157	78.29 225	93.73 167	95.08 169	85.73 113	84.75 180	91.90 193	79.88 84	96.92 208	83.83 130	82.51 236	93.89 195
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
APD-MVS		94.24 21	94.07 23	94.75 24	98.06 26	86.90 13	95.88 39	96.94 38	85.68 115	95.05 9	97.18 19	87.31 17	99.07 42	91.90 43	98.61 31	98.28 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
K. test v3		81.59 260	80.15 261	85.91 279	89.89 280	69.42 301	92.57 214	87.71 314	85.56 116	73.44 296	89.71 247	55.58 297	95.52 261	77.17 222	69.76 311	92.78 246

SixPastTwentyTwo		83.91 241	82.90 240	86.92 269	90.99 245	70.67 294	93.48 179	91.99 251	85.54 117	77.62 268	92.11 184	60.59 281	96.87 211	76.05 232	77.75 284	93.20 232

ITE_SJBPF				88.24 242	91.88 198	77.05 246		92.92 229	85.54 117	80.13 252	93.30 142	57.29 294	96.20 237	72.46 256	84.71 215	91.49 272

BH-RMVSNet		88.37 133	87.48 133	91.02 141	95.28 103	79.45 185	92.89 205	93.07 228	85.45 119	86.91 123	94.84 98	70.35 210	97.76 133	73.97 248	94.59 99	95.85 118

semantic-postprocess				88.18 244	91.71 204	76.87 248		92.65 237	85.40 120	81.44 235	90.54 233	66.21 251	95.00 282	81.04 165	81.05 258	92.66 248

GA-MVS		86.61 192	85.27 199	90.66 148	91.33 226	78.71 211	90.40 251	93.81 219	85.34 121	85.12 175	89.57 249	61.25 276	97.11 195	80.99 168	89.59 165	96.15 105

ACMM	84.12 9	89.14 115	88.48 114	91.12 134	94.65 126	81.22 139	95.31 57	96.12 92	85.31 122	85.92 141	94.34 107	70.19 213	98.06 119	85.65 106	88.86 176	94.08 189
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
xiu_mvs_v1_base_debu		90.64 79	90.05 81	92.40 90	93.97 151	84.46 65	93.32 183	95.46 139	85.17 123	92.25 51	94.03 117	70.59 205	98.57 85	90.97 52	94.67 95	94.18 181

xiu_mvs_v1_base		90.64 79	90.05 81	92.40 90	93.97 151	84.46 65	93.32 183	95.46 139	85.17 123	92.25 51	94.03 117	70.59 205	98.57 85	90.97 52	94.67 95	94.18 181

xiu_mvs_v1_base_debi		90.64 79	90.05 81	92.40 90	93.97 151	84.46 65	93.32 183	95.46 139	85.17 123	92.25 51	94.03 117	70.59 205	98.57 85	90.97 52	94.67 95	94.18 181

PHI-MVS		93.89 30	93.65 32	94.62 29	96.84 56	86.43 29	96.69 21	97.49 4	85.15 126	93.56 27	96.28 53	85.60 33	99.31 26	92.45 23	98.79 9	98.12 40

mvs_tets		88.06 143	87.28 139	90.38 165	90.94 249	79.88 171	95.22 70	95.66 123	85.10 127	84.21 197	93.94 124	63.53 265	97.40 170	88.50 74	88.40 185	93.87 198

XVG-ACMP-BASELINE		86.00 202	84.84 207	89.45 207	91.20 236	78.00 230	91.70 237	95.55 130	85.05 128	82.97 216	92.25 180	54.49 302	97.48 148	82.93 138	87.45 194	92.89 241

jajsoiax		88.24 137	87.50 132	90.48 161	90.89 253	80.14 164	95.31 57	95.65 125	84.97 129	84.24 196	94.02 120	65.31 257	97.42 163	88.56 73	88.52 180	93.89 195

v2v482		87.84 150	87.06 148	90.17 170	90.99 245	79.23 205	94.00 152	95.13 166	84.87 130	85.53 157	92.07 188	74.45 153	97.45 152	84.71 117	81.75 250	93.85 201

v148		87.04 184	86.32 174	89.21 213	90.94 249	77.26 244	93.71 170	94.43 193	84.84 131	84.36 192	90.80 227	76.04 128	97.05 200	82.12 151	79.60 279	93.31 229

v8		87.50 170	86.71 161	89.89 191	91.37 220	79.40 191	94.50 110	95.38 149	84.81 132	83.60 208	91.33 210	76.05 126	97.42 163	82.84 140	80.51 270	92.84 243

BH-untuned		88.60 129	88.13 124	90.01 188	95.24 106	78.50 219	93.29 188	94.15 202	84.75 133	84.46 186	93.40 137	75.76 138	97.40 170	77.59 217	94.52 101	94.12 185

OurMVSNet-221017-0		85.35 214	84.64 212	87.49 257	90.77 256	72.59 280	94.01 151	94.40 194	84.72 134	79.62 257	93.17 147	61.91 272	96.72 213	81.99 154	81.16 255	93.16 234

MVSFormer		91.68 64	91.30 60	92.80 77	93.86 154	83.88 78	95.96 36	95.90 107	84.66 135	91.76 63	94.91 92	77.92 107	97.30 177	89.64 65	97.11 61	97.24 74

test_djsdf		89.03 120	88.64 108	90.21 169	90.74 258	79.28 199	95.96 36	95.90 107	84.66 135	85.33 173	92.94 159	74.02 162	97.30 177	89.64 65	88.53 179	94.05 190

MVSTER		88.84 124	88.29 120	90.51 159	92.95 182	80.44 161	93.73 167	95.01 170	84.66 135	87.15 118	93.12 150	72.79 178	97.21 189	87.86 82	87.36 195	93.87 198

v1neww		87.98 144	87.25 141	90.16 171	91.38 218	79.41 187	94.37 124	95.28 154	84.48 138	85.77 144	91.53 206	76.12 122	97.45 152	84.45 121	81.89 245	93.61 218

v7new		87.98 144	87.25 141	90.16 171	91.38 218	79.41 187	94.37 124	95.28 154	84.48 138	85.77 144	91.53 206	76.12 122	97.45 152	84.45 121	81.89 245	93.61 218

v6		87.98 144	87.25 141	90.16 171	91.36 221	79.39 192	94.37 124	95.27 157	84.48 138	85.78 143	91.51 208	76.15 121	97.46 150	84.46 120	81.88 247	93.62 217

v7n		86.81 186	85.76 188	89.95 190	90.72 259	79.25 201	95.07 78	95.92 104	84.45 141	82.29 222	90.86 226	72.60 182	97.53 145	79.42 201	80.52 269	93.08 238

v1141		87.84 150	87.09 145	90.11 182	91.23 233	79.25 201	94.08 142	95.24 159	84.44 142	85.69 151	91.31 213	75.91 134	97.44 159	84.17 126	81.74 251	93.63 216

divwei89l23v2f112		87.84 150	87.09 145	90.10 184	91.23 233	79.24 203	94.09 140	95.24 159	84.44 142	85.70 149	91.31 213	75.91 134	97.44 159	84.17 126	81.73 252	93.64 214

v1		87.85 149	87.10 144	90.11 182	91.21 235	79.24 203	94.09 140	95.24 159	84.44 142	85.70 149	91.31 213	75.96 132	97.45 152	84.18 125	81.73 252	93.64 214

v748		86.27 198	85.28 198	89.25 212	90.26 271	77.58 243	94.89 87	95.50 137	84.28 145	81.41 236	90.46 236	72.57 183	97.32 176	79.81 193	78.36 282	92.84 243

CSCG		93.23 47	93.05 41	93.76 54	98.04 27	84.07 75	96.22 28	97.37 9	84.15 146	90.05 82	95.66 77	87.77 11	99.15 36	89.91 63	98.27 40	98.07 43

Baseline_NR-MVSNet		87.07 183	86.63 168	88.40 237	91.44 211	77.87 235	94.23 133	92.57 238	84.12 147	85.74 148	92.08 186	77.25 111	96.04 241	82.29 150	79.94 276	91.30 276

ab-mvs		89.41 109	88.35 115	92.60 82	95.15 107	82.65 114	92.20 226	95.60 127	83.97 148	88.55 95	93.70 136	74.16 160	98.21 102	82.46 147	89.37 167	96.94 87

FMVSNet3		87.40 173	86.11 179	91.30 130	93.79 159	83.64 84	94.20 134	94.81 184	83.89 149	84.37 189	91.87 194	68.45 238	96.56 220	78.23 211	85.36 209	93.70 212

test_normal		88.13 141	86.78 159	92.18 99	90.55 266	81.19 141	92.74 208	94.64 188	83.84 150	77.49 269	90.51 235	68.49 237	98.16 104	88.22 76	94.55 100	97.21 77

pm-mvs1		86.61 192	85.54 190	89.82 193	91.44 211	80.18 162	95.28 67	94.85 181	83.84 150	81.66 233	92.62 169	72.45 186	96.48 225	79.67 195	78.06 283	92.82 245

DI_MVS_plusplus_test		88.15 140	86.82 155	92.14 101	90.67 261	81.07 143	93.01 200	94.59 189	83.83 152	77.78 266	90.63 230	68.51 236	98.16 104	88.02 81	94.37 106	97.17 79

v10		87.25 177	86.38 171	89.85 192	91.19 238	79.50 178	94.48 111	95.45 143	83.79 153	83.62 207	91.19 217	75.13 146	97.42 163	81.94 155	80.60 266	92.63 249

testgi		80.94 270	80.20 260	83.18 294	87.96 299	66.29 309	91.28 244	90.70 285	83.70 154	78.12 263	92.84 161	51.37 308	90.82 312	63.34 299	82.46 237	92.43 254

V42		87.68 157	86.86 153	90.15 175	90.58 263	80.14 164	94.24 132	95.28 154	83.66 155	85.67 152	91.33 210	74.73 151	97.41 168	84.43 123	81.83 248	92.89 241

v52		86.50 194	85.53 193	89.39 208	89.17 284	78.99 208	94.72 100	95.54 132	83.59 156	82.10 226	90.60 232	71.59 191	97.45 152	82.52 143	79.99 275	91.73 268

V4		86.50 194	85.54 190	89.39 208	89.13 285	78.99 208	94.73 97	95.54 132	83.59 156	82.10 226	90.61 231	71.60 190	97.45 152	82.52 143	80.01 274	91.74 267

GBi-Net		87.26 175	85.98 183	91.08 137	94.01 146	83.10 96	95.14 75	94.94 174	83.57 158	84.37 189	91.64 197	66.59 247	96.34 233	78.23 211	85.36 209	93.79 203

test1		87.26 175	85.98 183	91.08 137	94.01 146	83.10 96	95.14 75	94.94 174	83.57 158	84.37 189	91.64 197	66.59 247	96.34 233	78.23 211	85.36 209	93.79 203

FMVSNet2		87.19 181	85.82 187	91.30 130	94.01 146	83.67 83	94.79 94	94.94 174	83.57 158	83.88 200	92.05 189	66.59 247	96.51 223	77.56 218	85.01 213	93.73 210

Patchmatch-test1		85.81 207	84.71 209	89.12 214	92.15 192	76.60 249	91.12 248	91.69 258	83.53 161	85.50 160	88.56 262	66.79 245	95.00 282	72.69 255	90.35 154	95.76 123

PVSNet_BlendedMVS		89.98 91	89.70 85	90.82 146	96.12 76	81.25 137	93.92 155	96.83 45	83.49 162	89.10 90	92.26 179	81.04 76	98.85 71	86.72 101	87.86 191	92.35 258

v7		87.75 155	86.96 151	90.12 177	91.20 236	79.50 178	94.28 130	95.46 139	83.45 163	85.75 146	91.56 205	75.13 146	97.43 161	83.60 132	82.18 240	93.42 227

test-LLR		85.87 204	85.41 195	87.25 262	90.95 247	71.67 285	89.55 262	89.88 299	83.41 164	84.54 184	87.95 270	67.25 242	95.11 279	81.82 157	93.37 122	94.97 143

test0.0.03 1		82.41 254	81.69 247	84.59 288	88.23 294	72.89 273	90.24 253	87.83 313	83.41 164	79.86 254	89.78 246	67.25 242	88.99 315	65.18 294	83.42 230	91.90 265

Test4		85.75 209	83.72 223	91.83 114	88.08 297	81.03 145	92.48 216	95.54 132	83.38 166	73.40 297	88.57 261	50.99 309	97.37 174	86.61 103	94.47 103	97.09 83

v1144		87.61 167	86.79 158	90.06 185	91.01 244	79.34 195	93.95 154	95.42 148	83.36 167	85.66 153	91.31 213	74.98 150	97.42 163	83.37 133	82.06 241	93.42 227

PVSNet_Blended_VisFu		91.38 67	90.91 68	92.80 77	96.39 66	83.17 95	94.87 90	96.66 61	83.29 168	89.27 88	94.46 106	80.29 81	99.17 33	87.57 86	95.37 88	96.05 112

IB-MVS	80.51 15	85.24 217	83.26 236	91.19 132	92.13 194	79.86 172	91.75 234	91.29 268	83.28 169	80.66 244	88.49 263	61.28 275	98.46 90	80.99 168	79.46 280	95.25 137
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
IterMVS		84.88 226	83.98 218	87.60 253	91.44 211	76.03 255	90.18 255	92.41 240	83.24 170	81.06 240	90.42 237	66.60 246	94.28 287	79.46 197	80.98 263	92.48 252
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+		89.41 109	88.64 108	91.71 119	94.74 119	80.81 152	93.54 177	95.10 167	83.11 171	86.82 126	90.67 229	79.74 87	97.75 136	80.51 177	93.55 115	96.57 97

WTY-MVS		89.60 100	88.92 103	91.67 120	95.47 97	81.15 142	92.38 220	94.78 185	83.11 171	89.06 92	94.32 109	78.67 98	96.61 219	81.57 161	90.89 150	97.24 74

V9		84.77 230	83.50 231	88.58 228	91.33 226	79.46 183	93.75 165	94.00 211	83.07 173	76.07 282	86.43 285	75.97 131	95.37 271	79.91 190	70.93 307	90.91 285

v15		84.79 228	83.53 229	88.57 231	91.30 232	79.41 187	93.70 171	94.01 208	83.06 174	76.27 276	86.42 288	76.03 129	95.38 270	80.01 185	71.00 304	90.92 284

v12		84.74 231	83.46 232	88.58 228	91.32 228	79.50 178	93.75 165	94.01 208	83.06 174	75.98 284	86.41 289	75.82 137	95.36 273	79.87 191	70.89 308	90.89 287

V14		84.79 228	83.52 230	88.57 231	91.32 228	79.43 186	93.72 169	94.01 208	83.06 174	76.22 277	86.43 285	76.01 130	95.37 271	79.96 187	70.99 305	90.91 285

v17		84.93 225	83.70 224	88.62 225	91.36 221	79.48 181	93.83 158	94.03 207	83.04 177	76.51 275	86.57 284	76.05 126	95.42 268	80.31 183	71.65 301	90.96 281

v16		84.96 223	83.74 222	88.62 225	91.40 216	79.48 181	93.83 158	94.04 205	83.03 178	76.54 274	86.59 283	76.11 125	95.42 268	80.33 181	71.80 299	90.95 283

v13		84.72 233	83.44 234	88.58 228	91.31 231	79.52 177	93.77 163	94.00 211	83.03 178	75.85 285	86.38 290	75.84 136	95.35 274	79.83 192	70.95 306	90.87 288

LTVRE_ROB	82.13 13	86.26 199	84.90 205	90.34 167	94.44 134	81.50 129	92.31 222	94.89 179	83.03 178	79.63 256	92.67 167	69.69 217	97.79 131	71.20 261	86.26 203	91.72 269
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
v18		84.97 222	83.76 220	88.60 227	91.36 221	79.41 187	93.82 160	94.04 205	83.00 181	76.61 273	86.60 282	76.19 120	95.43 267	80.39 178	71.79 300	90.96 281

UnsupCasMVSNet_eth		80.07 273	78.27 275	85.46 282	85.24 307	72.63 279	88.45 279	94.87 180	82.99 182	71.64 305	88.07 269	56.34 296	91.75 309	73.48 252	63.36 322	92.01 264

XXY-MVS		87.65 158	86.85 154	90.03 186	92.14 193	80.60 157	93.76 164	95.23 162	82.94 183	84.60 182	94.02 120	74.27 155	95.49 265	81.04 165	83.68 225	94.01 192

testing_2		83.40 247	81.02 252	90.56 151	85.06 308	80.51 159	91.37 243	95.57 128	82.92 184	67.06 313	85.54 296	49.47 312	97.24 185	86.74 98	85.44 208	93.93 193

mvs_anonymous		89.37 112	89.32 93	89.51 206	93.47 165	74.22 262	91.65 239	94.83 183	82.91 185	85.45 163	93.79 132	81.23 75	96.36 232	86.47 104	94.09 108	97.94 51

BH-w/o		87.57 168	87.05 149	89.12 214	94.90 116	77.90 233	92.41 218	93.51 222	82.89 186	83.70 205	91.34 209	75.75 139	97.07 198	75.49 234	93.49 117	92.39 256

v11		84.67 235	83.41 235	88.44 236	91.32 228	79.13 206	93.69 174	93.99 213	82.81 187	76.20 278	86.24 292	75.48 142	95.35 274	79.53 196	71.48 303	90.85 289

AdaColmap		89.89 96	89.07 99	92.37 93	97.41 40	83.03 99	94.42 117	95.92 104	82.81 187	86.34 135	94.65 102	73.89 163	99.02 51	80.69 172	95.51 84	95.05 140

TransMVSNet (Re)		84.43 237	83.06 238	88.54 233	91.72 203	78.44 220	95.18 72	92.82 232	82.73 189	79.67 255	92.12 182	73.49 169	95.96 246	71.10 264	68.73 315	91.21 277

PatchFormer-LS_test		86.02 201	85.13 200	88.70 222	91.52 208	74.12 265	91.19 247	92.09 246	82.71 190	84.30 195	87.24 279	70.87 200	96.98 204	81.04 165	85.17 212	95.00 142

DP-MVS Recon		91.95 58	91.28 61	93.96 46	98.33 17	85.92 42	94.66 105	96.66 61	82.69 191	90.03 83	95.82 72	82.30 61	99.03 48	84.57 118	96.48 75	96.91 88

v1192		87.25 177	86.33 173	90.00 189	90.76 257	79.04 207	93.80 161	95.48 138	82.57 192	85.48 161	91.18 218	73.38 173	97.42 163	82.30 149	82.06 241	93.53 222

API-MVS		90.66 78	90.07 80	92.45 89	96.36 67	84.57 60	96.06 33	95.22 164	82.39 193	89.13 89	94.27 114	80.32 80	98.46 90	80.16 184	96.71 68	94.33 178

MAR-MVS		90.30 85	89.37 92	93.07 68	96.61 60	84.48 64	95.68 47	95.67 121	82.36 194	87.85 103	92.85 160	76.63 118	98.80 74	80.01 185	96.68 69	95.91 115
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
TAMVS		89.21 114	88.29 120	91.96 107	93.71 160	82.62 115	93.30 187	94.19 200	82.22 195	87.78 110	93.94 124	78.83 95	96.95 207	77.70 216	92.98 129	96.32 101

ACMH+	81.04 14	85.05 220	83.46 232	89.82 193	94.66 125	79.37 193	94.44 115	94.12 204	82.19 196	78.04 264	92.82 163	58.23 291	97.54 144	73.77 250	82.90 233	92.54 250

ACMH	80.38 17	85.36 213	83.68 225	90.39 163	94.45 133	80.63 155	94.73 97	94.85 181	82.09 197	77.24 270	92.65 168	60.01 285	97.58 141	72.25 257	84.87 214	92.96 239
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
anonymousdsp		87.84 150	87.09 145	90.12 177	89.13 285	80.54 158	94.67 104	95.55 130	82.05 198	83.82 202	92.12 182	71.47 194	97.15 191	87.15 93	87.80 192	92.67 247

PVSNet_Blended		90.73 77	90.32 75	91.98 106	96.12 76	81.25 137	92.55 215	96.83 45	82.04 199	89.10 90	92.56 170	81.04 76	98.85 71	86.72 101	95.91 79	95.84 119

agg_prior1		93.29 42	92.97 44	94.26 41	97.38 41	85.92 42	93.92 155	96.72 55	81.96 200	92.16 54	96.23 55	87.85 10	98.97 59	91.95 39	98.55 35	97.90 56

CDS-MVSNet		89.45 106	88.51 110	92.29 96	93.62 162	83.61 86	93.01 200	94.68 187	81.95 201	87.82 109	93.24 145	78.69 97	96.99 203	80.34 180	93.23 125	96.28 102
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v144192		87.19 181	86.35 172	89.74 197	90.64 262	78.24 226	93.92 155	95.43 146	81.93 202	85.51 159	91.05 224	74.21 158	97.45 152	82.86 139	81.56 254	93.53 222

PAPR		90.02 90	89.27 96	92.29 96	95.78 88	80.95 148	92.68 210	96.22 85	81.91 203	86.66 128	93.75 135	82.23 62	98.44 92	79.40 202	94.79 94	97.48 70

v1921920		86.97 185	86.06 182	89.69 200	90.53 267	78.11 229	93.80 161	95.43 146	81.90 204	85.33 173	91.05 224	72.66 180	97.41 168	82.05 153	81.80 249	93.53 222

CPTT-MVS		91.99 57	91.80 56	92.55 84	98.24 21	81.98 124	96.76 19	96.49 70	81.89 205	90.24 79	96.44 49	78.59 99	98.61 83	89.68 64	97.85 51	97.06 84

train_agg		93.44 38	93.08 40	94.52 32	97.53 34	86.49 27	94.07 144	96.78 49	81.86 206	92.77 38	96.20 57	87.63 14	99.12 39	92.14 33	98.69 19	97.94 51

test_8					97.49 37	86.30 35	94.02 150	96.76 52	81.86 206	92.70 42	96.20 57	87.63 14	99.02 51

v1240		86.78 188	85.85 186	89.56 202	90.45 268	77.79 237	93.61 175	95.37 151	81.65 208	85.43 166	91.15 220	71.50 193	97.43 161	81.47 162	82.05 243	93.47 226

FMVSNet1		85.85 205	84.11 215	91.08 137	92.81 184	83.10 96	95.14 75	94.94 174	81.64 209	82.68 219	91.64 197	59.01 289	96.34 233	75.37 236	83.78 222	93.79 203

PatchmatchNet		85.85 205	84.70 210	89.29 211	91.76 202	75.54 258	88.49 278	91.30 267	81.63 210	85.05 176	88.70 259	71.71 188	96.24 236	74.61 244	89.05 174	96.08 109
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TEST9					97.53 34	86.49 27	94.07 144	96.78 49	81.61 211	92.77 38	96.20 57	87.71 13	99.12 39

sss		88.93 123	88.26 122	90.94 145	94.05 144	80.78 153	91.71 236	95.38 149	81.55 212	88.63 94	93.91 128	75.04 149	95.47 266	82.47 146	91.61 139	96.57 97

HY-MVS	83.01 12	89.03 120	87.94 128	92.29 96	94.86 117	82.77 106	92.08 231	94.49 191	81.52 213	86.93 122	92.79 166	78.32 104	98.23 100	79.93 188	90.55 151	95.88 117

agg_prior3		93.27 43	92.89 46	94.40 38	97.49 37	86.12 39	94.07 144	96.73 53	81.46 214	92.46 50	96.05 65	86.90 21	99.15 36	92.14 33	98.69 19	97.94 51

CNLPA		89.07 117	87.98 126	92.34 94	96.87 55	84.78 56	94.08 142	93.24 225	81.41 215	84.46 186	95.13 89	75.57 141	96.62 217	77.21 221	93.84 112	95.61 128

EPMVS		83.90 242	82.70 243	87.51 255	90.23 273	72.67 277	88.62 277	81.96 328	81.37 216	85.01 177	88.34 265	66.31 250	94.45 285	75.30 237	87.12 198	95.43 132

test20.03		79.95 274	79.08 271	82.55 297	85.79 305	67.74 306	91.09 249	91.08 273	81.23 217	74.48 292	89.96 244	61.63 273	90.15 313	60.08 307	76.38 288	89.76 295

TR-MVS		86.78 188	85.76 188	89.82 193	94.37 135	78.41 221	92.47 217	92.83 231	81.11 218	86.36 134	92.40 174	68.73 233	97.48 148	73.75 251	89.85 161	93.57 221

VDDNet		89.56 102	88.49 113	92.76 79	95.07 108	82.09 121	96.30 26	93.19 226	81.05 219	91.88 59	96.86 28	61.16 279	98.33 97	88.43 75	92.49 135	97.84 58

tpm		84.73 232	84.02 216	86.87 272	90.33 269	68.90 302	89.06 272	89.94 297	80.85 220	85.75 146	89.86 245	68.54 235	95.97 245	77.76 215	84.05 221	95.75 124

DWT-MVSNet_test		84.95 224	83.68 225	88.77 219	91.43 214	73.75 268	91.74 235	90.98 277	80.66 221	83.84 201	87.36 277	62.44 268	97.11 195	78.84 206	85.81 205	95.46 131

diffmvs		89.07 117	88.32 118	91.34 128	93.24 171	79.79 174	92.29 223	94.98 173	80.24 222	87.38 117	92.45 172	78.02 105	97.33 175	83.29 134	92.93 130	96.91 88

jason		90.80 75	90.10 79	92.90 74	93.04 178	83.53 87	93.08 197	94.15 202	80.22 223	91.41 68	94.91 92	76.87 113	97.93 127	90.28 62	96.90 64	97.24 74
jason: jason.
tpmrst		85.35 214	84.99 201	86.43 275	90.88 254	67.88 305	88.71 275	91.43 265	80.13 224	86.08 140	88.80 257	73.05 175	96.02 243	82.48 145	83.40 231	95.40 133

CDPH-MVS		92.83 51	92.30 53	94.44 34	97.79 31	86.11 40	94.06 147	96.66 61	80.09 225	92.77 38	96.63 40	86.62 23	99.04 47	87.40 88	98.66 26	98.17 35

PM-MVS		78.11 282	76.12 284	84.09 293	83.54 313	70.08 298	88.97 273	85.27 322	79.93 226	74.73 290	86.43 285	34.70 327	93.48 295	79.43 200	72.06 298	88.72 305

lupinMVS		90.92 74	90.21 76	93.03 69	93.86 154	83.88 78	92.81 206	93.86 216	79.84 227	91.76 63	94.29 111	77.92 107	98.04 120	90.48 61	97.11 61	97.17 79

PatchMatch-RL		86.77 190	85.54 190	90.47 162	95.88 85	82.71 112	90.54 250	92.31 241	79.82 228	84.32 193	91.57 204	68.77 232	96.39 230	73.16 253	93.48 119	92.32 259

PLC	84.53 7	89.06 119	88.03 125	92.15 100	97.27 48	82.69 113	94.29 129	95.44 145	79.71 229	84.01 199	94.18 116	76.68 117	98.75 76	77.28 220	93.41 120	95.02 141
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
F-COLMAP		87.95 147	86.80 157	91.40 127	96.35 68	80.88 150	94.73 97	95.45 143	79.65 230	82.04 229	94.61 103	71.13 196	98.50 88	76.24 230	91.05 144	94.80 159

MIMVSNet		82.59 253	80.53 256	88.76 220	91.51 209	78.32 223	86.57 292	90.13 292	79.32 231	80.70 243	88.69 260	52.98 306	93.07 302	66.03 291	88.86 176	94.90 154

test-mter		84.54 236	83.64 227	87.25 262	90.95 247	71.67 285	89.55 262	89.88 299	79.17 232	84.54 184	87.95 270	55.56 298	95.11 279	81.82 157	93.37 122	94.97 143

MDA-MVSNet-bldmvs		78.85 281	76.31 282	86.46 274	89.76 281	73.88 267	88.79 274	90.42 286	79.16 233	59.18 321	88.33 266	60.20 283	94.04 289	62.00 303	68.96 313	91.48 273

tpmvs		83.35 248	82.07 245	87.20 266	91.07 243	71.00 292	88.31 280	91.70 257	78.91 234	80.49 247	87.18 280	69.30 224	97.08 197	68.12 284	83.56 227	93.51 225

原ACMM1				92.01 103	97.34 43	81.05 144		96.81 47	78.89 235	90.45 77	95.92 68	82.65 57	98.84 73	80.68 173	98.26 41	96.14 106

MSDG		84.86 227	83.09 237	90.14 176	93.80 157	80.05 167	89.18 271	93.09 227	78.89 235	78.19 262	91.91 192	65.86 256	97.27 181	68.47 279	88.45 182	93.11 236

PAPM		86.68 191	85.39 196	90.53 152	93.05 177	79.33 198	89.79 261	94.77 186	78.82 237	81.95 230	93.24 145	76.81 114	97.30 177	66.94 286	93.16 126	94.95 153

PVSNet	78.82 18	85.55 211	84.65 211	88.23 243	94.72 121	71.93 283	87.12 289	92.75 234	78.80 238	84.95 178	90.53 234	64.43 262	96.71 215	74.74 242	93.86 111	96.06 111

MVP-Stereo		85.97 203	84.86 206	89.32 210	90.92 251	82.19 120	92.11 229	94.19 200	78.76 239	78.77 261	91.63 200	68.38 239	96.56 220	75.01 241	93.95 109	89.20 300
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OpenMVS	83.78 11	88.74 127	87.29 138	93.08 66	92.70 185	85.39 50	96.57 22	96.43 73	78.74 240	80.85 241	96.07 64	69.64 218	99.01 53	78.01 214	96.65 70	94.83 157

MDTV_nov1_ep13			83.56 228		91.69 206	69.93 299	87.75 285	91.54 262	78.60 241	84.86 179	88.90 255	69.54 219	96.03 242	70.25 266	88.93 175

Patchmatch-RL test		81.67 258	79.96 262	86.81 273	85.42 306	71.23 288	82.17 316	87.50 317	78.47 242	77.19 271	82.50 307	70.81 202	93.48 295	82.66 142	72.89 296	95.71 125

QAPM		89.51 103	88.15 123	93.59 56	94.92 114	84.58 59	96.82 18	96.70 57	78.43 243	83.41 212	96.19 60	73.18 174	99.30 27	77.11 223	96.54 73	96.89 90

1314		87.51 169	86.57 169	90.34 167	92.42 189	79.74 176	92.63 211	95.35 153	78.35 244	80.14 251	91.62 201	74.05 161	97.15 191	81.05 164	93.53 116	94.12 185

CR-MVSNet		85.35 214	83.76 220	90.12 177	90.58 263	79.34 195	85.24 301	91.96 253	78.27 245	85.55 155	87.87 273	71.03 198	95.61 257	73.96 249	89.36 168	95.40 133

USDC		82.76 250	81.26 251	87.26 261	91.17 239	74.55 261	89.27 268	93.39 224	78.26 246	75.30 287	92.08 186	54.43 303	96.63 216	71.64 258	85.79 207	90.61 291

new-patchmatchnet		76.41 285	75.17 285	80.13 300	82.65 317	59.61 319	87.66 286	91.08 273	78.23 247	69.85 307	83.22 304	54.76 301	91.63 311	64.14 298	64.89 318	89.16 301

1112_ss		88.42 131	87.33 137	91.72 118	94.92 114	80.98 146	92.97 203	94.54 190	78.16 248	83.82 202	93.88 129	78.78 96	97.91 128	79.45 198	89.41 166	96.26 103

MIMVSNet1		79.38 278	77.28 278	85.69 280	86.35 304	73.67 269	91.61 240	92.75 234	78.11 249	72.64 301	88.12 268	48.16 314	91.97 308	60.32 306	77.49 285	91.43 274

MS-PatchMatch		85.05 220	84.16 214	87.73 251	91.42 215	78.51 218	91.25 246	93.53 221	77.50 250	80.15 250	91.58 202	61.99 271	95.51 262	75.69 233	94.35 107	89.16 301

AllTest		83.42 245	81.39 249	89.52 204	95.01 109	77.79 237	93.12 194	90.89 280	77.41 251	76.12 280	93.34 138	54.08 304	97.51 146	68.31 281	84.27 219	93.26 230

TestCases				89.52 204	95.01 109	77.79 237		90.89 280	77.41 251	76.12 280	93.34 138	54.08 304	97.51 146	68.31 281	84.27 219	93.26 230

TESTMET0.1,1		83.74 244	82.85 241	86.42 276	89.96 278	71.21 289	89.55 262	87.88 312	77.41 251	83.37 213	87.31 278	56.71 295	93.65 293	80.62 174	92.85 133	94.40 177

gm-plane-assit					89.60 283	68.00 304			77.28 254		88.99 254		97.57 142	79.44 199

EG-PatchMatch MVS		82.37 255	80.34 257	88.46 235	90.27 270	79.35 194	92.80 207	94.33 197	77.14 255	73.26 298	90.18 240	47.47 316	96.72 213	70.25 266	87.32 197	89.30 298

FMVSNet5		81.52 262	79.60 266	87.27 260	91.17 239	77.95 231	91.49 241	92.26 243	76.87 256	76.16 279	87.91 272	51.67 307	92.34 304	67.74 285	81.16 255	91.52 271

TDRefinement		79.81 275	77.34 277	87.22 265	79.24 323	75.48 259	93.12 194	92.03 249	76.45 257	75.01 288	91.58 202	49.19 313	96.44 228	70.22 268	69.18 312	89.75 296

LF4IMVS		80.37 272	79.07 272	84.27 292	86.64 303	69.87 300	89.39 267	91.05 275	76.38 258	74.97 289	90.00 242	47.85 315	94.25 288	74.55 245	80.82 265	88.69 306

TAPA-MVS	84.62 6	88.16 139	87.01 150	91.62 121	96.64 59	80.65 154	94.39 120	96.21 88	76.38 258	86.19 138	95.44 80	79.75 86	98.08 117	62.75 302	95.29 90	96.13 107
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
dp		81.47 263	80.23 259	85.17 285	89.92 279	65.49 312	86.74 290	90.10 293	76.30 260	81.10 239	87.12 281	62.81 266	95.92 247	68.13 283	79.88 277	94.09 188

CostFormer		85.77 208	84.94 204	88.26 241	91.16 241	72.58 281	89.47 266	91.04 276	76.26 261	86.45 132	89.97 243	70.74 203	96.86 212	82.35 148	87.07 200	95.34 136

RPSCF		85.07 219	84.27 213	87.48 258	92.91 183	70.62 295	91.69 238	92.46 239	76.20 262	82.67 220	95.22 86	63.94 264	97.29 180	77.51 219	85.80 206	94.53 170

Test_1112_low_res		87.65 158	86.51 170	91.08 137	94.94 113	79.28 199	91.77 233	94.30 198	76.04 263	83.51 210	92.37 175	77.86 109	97.73 137	78.69 207	89.13 173	96.22 104

pmmvs4		85.43 212	83.86 219	90.16 171	90.02 277	82.97 103	90.27 252	92.67 236	75.93 264	80.73 242	91.74 196	71.05 197	95.73 256	78.85 205	83.46 229	91.78 266

LS3D		87.89 148	86.32 174	92.59 83	96.07 80	82.92 104	95.23 69	94.92 178	75.66 265	82.89 217	95.98 66	72.48 184	99.21 30	68.43 280	95.23 92	95.64 127

pmmvs5		84.21 238	82.84 242	88.34 239	88.95 288	76.94 247	92.41 218	91.91 255	75.63 266	80.28 248	91.18 218	64.59 261	95.57 259	77.09 224	83.47 228	92.53 251

pmmvs-eth3d		80.97 269	78.72 274	87.74 250	84.99 309	79.97 170	90.11 256	91.65 259	75.36 267	73.51 295	86.03 293	59.45 287	93.96 290	75.17 238	72.21 297	89.29 299

test_0402		81.30 266	79.17 270	87.67 252	93.19 173	78.17 227	92.98 202	91.71 256	75.25 268	76.02 283	90.31 238	59.23 288	96.37 231	50.22 319	83.63 226	88.47 311

COLMAP_ROB	80.39 16	83.96 240	82.04 246	89.74 197	95.28 103	79.75 175	94.25 131	92.28 242	75.17 269	78.02 265	93.77 133	58.60 290	97.84 130	65.06 295	85.92 204	91.63 270
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TinyColmap		79.76 276	77.69 276	85.97 278	91.71 204	73.12 271	89.55 262	90.36 288	75.03 270	72.03 303	90.19 239	46.22 318	96.19 238	63.11 300	81.03 259	88.59 307

DP-MVS		87.25 177	85.36 197	92.90 74	97.65 32	83.24 93	94.81 93	92.00 250	74.99 271	81.92 231	95.00 91	72.66 180	99.05 44	66.92 288	92.33 136	96.40 99

PatchT		82.68 252	81.27 250	86.89 271	90.09 275	70.94 293	84.06 308	90.15 291	74.91 272	85.63 154	83.57 302	69.37 220	94.87 284	65.19 293	88.50 181	94.84 156

CHOSEN 280x420		85.15 218	83.99 217	88.65 223	92.47 188	78.40 222	79.68 321	92.76 233	74.90 273	81.41 236	89.59 248	69.85 216	95.51 262	79.92 189	95.29 90	92.03 263

gg-mvs-nofinetune		81.77 257	79.37 267	88.99 217	90.85 255	77.73 240	86.29 293	79.63 332	74.88 274	83.19 215	69.05 324	60.34 282	96.11 240	75.46 235	94.64 98	93.11 236

pmmvs6		83.42 245	81.60 248	88.87 218	88.01 298	77.87 235	94.96 82	94.24 199	74.67 275	78.80 260	91.09 223	60.17 284	96.49 224	77.06 225	75.40 291	92.23 261

CHOSEN 1792x2688		88.84 124	87.69 130	92.30 95	96.14 75	81.42 134	90.01 257	95.86 111	74.52 276	87.41 114	93.94 124	75.46 143	98.36 93	80.36 179	95.53 83	97.12 82

MDA-MVSNet_test_wron		79.21 280	77.19 280	85.29 283	88.22 295	72.77 276	85.87 296	90.06 294	74.34 277	62.62 320	87.56 276	66.14 253	91.99 307	66.90 289	73.01 294	91.10 280

YYNet1		79.22 279	77.20 279	85.28 284	88.20 296	72.66 278	85.87 296	90.05 296	74.33 278	62.70 319	87.61 275	66.09 254	92.03 306	66.94 286	72.97 295	91.15 278

无先验							93.28 189	96.26 81	73.95 279				99.05 44	80.56 175		96.59 96

Anonymous20231206		81.03 268	79.77 264	84.82 287	87.85 301	70.26 297	91.42 242	92.08 247	73.67 280	77.75 267	89.25 252	62.43 269	93.08 301	61.50 304	82.00 244	91.12 279

PCF-MVS	84.11 10	87.74 156	86.08 181	92.70 80	94.02 145	84.43 69	89.27 268	95.87 110	73.62 281	84.43 188	94.33 108	78.48 102	98.86 68	70.27 265	94.45 104	94.81 158
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HyFIR lowres test		88.09 142	86.81 156	91.93 109	96.00 82	80.63 155	90.01 257	95.79 114	73.42 282	87.68 112	92.10 185	73.86 164	97.96 124	80.75 171	91.70 138	97.19 78

MDTV_nov1_ep13_2view						55.91 327	87.62 287		73.32 283	84.59 183		70.33 211		74.65 243		95.50 129

JIA-IIPM		81.04 267	78.98 273	87.25 262	88.64 290	73.48 270	81.75 317	89.61 303	73.19 284	82.05 228	73.71 321	66.07 255	95.87 250	71.18 263	84.60 216	92.41 255

cascas		86.43 197	84.98 202	90.80 147	92.10 195	80.92 149	90.24 253	95.91 106	73.10 285	83.57 209	88.39 264	65.15 258	97.46 150	84.90 114	91.43 140	94.03 191

ANet_high		58.88 304	54.22 307	72.86 311	56.50 339	56.67 324	80.75 319	86.00 319	73.09 286	37.39 330	64.63 328	22.17 335	79.49 333	43.51 328	23.96 334	82.43 320

tpmp4_e23		83.87 243	82.33 244	88.48 234	91.46 210	72.82 274	89.82 260	91.57 261	73.02 287	81.86 232	89.05 253	66.20 252	96.97 205	71.57 259	86.39 202	95.66 126

ADS-MVSNet2		81.66 259	79.71 265	87.50 256	91.35 224	74.19 263	83.33 312	88.48 309	72.90 288	82.24 224	85.77 294	64.98 259	93.20 299	64.57 296	83.74 223	95.12 138

ADS-MVSNet		81.56 261	79.78 263	86.90 270	91.35 224	71.82 284	83.33 312	89.16 305	72.90 288	82.24 224	85.77 294	64.98 259	93.76 291	64.57 296	83.74 223	95.12 138

PVSNet_0	73.20 20	77.22 283	74.83 286	84.37 290	90.70 260	71.10 290	83.09 314	89.67 302	72.81 290	73.93 294	83.13 305	60.79 280	93.70 292	68.54 278	50.84 327	88.30 312

testdata				90.49 160	96.40 65	77.89 234		95.37 151	72.51 291	93.63 23	96.69 36	82.08 66	97.65 138	83.08 135	97.39 58	95.94 114

PMMVS		85.71 210	84.96 203	87.95 248	88.90 289	77.09 245	88.68 276	90.06 294	72.32 292	86.47 129	90.76 228	72.15 187	94.40 286	81.78 159	93.49 117	92.36 257

testus		74.41 289	73.35 287	77.59 306	82.49 318	57.08 322	86.02 294	90.21 290	72.28 293	72.89 300	84.32 299	37.08 325	86.96 321	52.24 315	82.65 235	88.73 304

Patchmtry		82.71 251	80.93 254	88.06 246	90.05 276	76.37 252	84.74 303	91.96 253	72.28 293	81.32 238	87.87 273	71.03 198	95.50 264	68.97 277	80.15 272	92.32 259

tpm2		84.08 239	82.94 239	87.48 258	91.39 217	71.27 287	89.23 270	90.37 287	71.95 295	84.64 181	89.33 251	67.30 241	96.55 222	75.17 238	87.09 199	94.63 162

UnsupCasMVSNet_bld		76.23 286	73.27 288	85.09 286	83.79 312	72.92 272	85.65 300	93.47 223	71.52 296	68.84 309	79.08 317	49.77 310	93.21 298	66.81 290	60.52 324	89.13 303

RPMNet		83.18 249	80.87 255	90.12 177	90.58 263	79.34 195	85.24 301	90.78 283	71.44 297	85.55 155	82.97 306	70.87 200	95.61 257	61.01 305	89.36 168	95.40 133

test2356		74.50 288	73.27 288	78.20 302	80.81 319	59.84 317	83.76 311	88.33 311	71.43 298	72.37 302	81.84 310	45.60 319	86.26 323	50.97 317	84.32 217	88.50 308

旧先验2							93.36 182		71.25 299	94.37 11			97.13 194	86.74 98

testpf		71.41 295	72.11 292	69.30 315	84.53 310	59.79 318	62.74 331	83.14 325	71.11 300	68.83 310	81.57 312	46.70 317	84.83 328	74.51 246	75.86 290	63.30 326

新几何1				93.10 65	97.30 45	84.35 71		95.56 129	71.09 301	91.26 70	96.24 54	82.87 56	98.86 68	79.19 203	98.10 45	96.07 110

1121		90.42 84	89.49 88	93.20 61	97.27 48	84.46 65	92.63 211	95.51 136	71.01 302	91.20 71	96.21 56	82.92 55	99.05 44	80.56 175	98.07 46	96.10 108

Patchmatch-test		81.37 264	79.30 268	87.58 254	90.92 251	74.16 264	80.99 318	87.68 315	70.52 303	76.63 272	88.81 256	71.21 195	92.76 303	60.01 309	86.93 201	95.83 120

114514_t		89.51 103	88.50 111	92.54 85	98.11 23	81.99 123	95.16 74	96.36 78	70.19 304	85.81 142	95.25 85	76.70 116	98.63 81	82.07 152	96.86 66	97.00 85

test1235678		72.22 292	70.31 293	77.93 305	78.04 324	58.04 321	85.76 298	89.80 301	70.15 305	63.43 318	80.20 315	42.24 322	87.24 320	48.68 321	74.50 292	88.50 308

N_pmnet		68.89 297	68.44 298	70.23 313	89.07 287	28.79 341	88.06 281	19.50 343	69.47 306	71.86 304	84.93 297	61.24 277	91.75 309	54.70 313	77.15 287	90.15 294

OpenMVS_ROB	74.94 19	79.51 277	77.03 281	86.93 268	87.00 302	76.23 254	92.33 221	90.74 284	68.93 307	74.52 291	88.23 267	49.58 311	96.62 217	57.64 311	84.29 218	87.94 313

test222					96.55 63	81.70 126	92.22 225	95.01 170	68.36 308	90.20 80	96.14 62	80.26 82			97.80 52	96.05 112

LP		75.51 287	72.15 291	85.61 281	87.86 300	73.93 266	80.20 320	88.43 310	67.39 309	70.05 306	80.56 314	58.18 292	93.18 300	46.28 325	70.36 310	89.71 297

1111		70.54 296	69.71 295	73.04 310	79.30 321	44.83 334	84.23 306	88.96 306	67.33 310	65.42 315	82.28 308	41.11 323	88.11 318	47.12 323	71.60 302	86.19 315

.test1245		57.63 306	61.79 303	45.14 324	79.30 321	44.83 334	84.23 306	88.96 306	67.33 310	65.42 315	82.28 308	41.11 323	88.11 318	47.12 323	0.39 338	2.46 337

MVS		87.44 171	86.10 180	91.44 126	92.61 187	83.62 85	92.63 211	95.66 123	67.26 312	81.47 234	92.15 181	77.95 106	98.22 101	79.71 194	95.48 85	92.47 253

tpm cat1		81.96 256	80.27 258	87.01 267	91.09 242	71.02 291	87.38 288	91.53 263	66.25 313	80.17 249	86.35 291	68.22 240	96.15 239	69.16 276	82.29 238	93.86 200

CVMVSNet		84.69 234	84.79 208	84.37 290	91.84 199	64.92 313	93.70 171	91.47 264	66.19 314	86.16 139	95.28 83	67.18 244	93.33 297	80.89 170	90.42 153	94.88 155

testmv		65.49 299	62.66 300	73.96 309	68.78 330	53.14 329	84.70 304	88.56 308	65.94 315	52.35 324	74.65 320	25.02 333	85.14 326	43.54 327	60.40 325	83.60 316

test12356		64.99 300	63.78 299	68.61 317	72.69 327	39.14 337	78.46 322	87.61 316	64.91 316	55.77 322	77.48 318	28.10 330	85.59 325	44.69 326	64.35 319	81.12 321

CMPMVS	59.16 21	80.52 271	79.20 269	84.48 289	83.98 311	67.63 307	89.95 259	93.84 218	64.79 317	66.81 314	91.14 221	57.93 293	95.17 277	76.25 229	88.10 187	90.65 290
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		81.32 265	80.95 253	82.42 298	88.50 292	63.67 314	93.32 183	91.33 266	64.02 318	80.57 246	92.83 162	61.21 278	92.27 305	76.34 228	80.38 271	91.32 275

no-one		61.56 302	56.58 304	76.49 308	67.80 333	62.76 316	78.13 323	86.11 318	63.16 319	43.24 328	64.70 327	26.12 332	88.95 316	50.84 318	29.15 330	77.77 323

new_pmnet		72.15 293	70.13 294	78.20 302	82.95 316	65.68 310	83.91 309	82.40 327	62.94 320	64.47 317	79.82 316	42.85 321	86.26 323	57.41 312	74.44 293	82.65 319

Anonymous20231211		72.97 291	69.63 296	83.00 296	83.05 315	66.91 308	92.69 209	89.45 304	61.06 321	67.50 312	83.46 303	34.34 328	93.61 294	51.11 316	63.97 320	88.48 310

DSMNet-mixed		76.94 284	76.29 283	78.89 301	83.10 314	56.11 326	87.78 284	79.77 331	60.65 322	75.64 286	88.71 258	61.56 274	88.34 317	60.07 308	89.29 170	92.21 262

pmmvs3		71.81 294	68.71 297	81.11 299	75.86 325	70.42 296	86.74 290	83.66 324	58.95 323	68.64 311	80.89 313	36.93 326	89.52 314	63.10 301	63.59 321	83.39 317

MVS-HIRNet		73.70 290	72.20 290	78.18 304	91.81 201	56.42 325	82.94 315	82.58 326	55.24 324	68.88 308	66.48 325	55.32 300	95.13 278	58.12 310	88.42 184	83.01 318

PMMVS2		59.60 303	56.40 305	69.21 316	68.83 329	46.58 332	73.02 329	77.48 335	55.07 325	49.21 326	72.95 323	17.43 339	80.04 331	49.32 320	44.33 328	80.99 322

FPMVS		64.63 301	62.55 301	70.88 312	70.80 328	56.71 323	84.42 305	84.42 323	51.78 326	49.57 325	81.61 311	23.49 334	81.48 330	40.61 330	76.25 289	74.46 325

LCM-MVSNet		66.00 298	62.16 302	77.51 307	64.51 335	58.29 320	83.87 310	90.90 279	48.17 327	54.69 323	73.31 322	16.83 340	86.75 322	65.47 292	61.67 323	87.48 314

DeepMVS_CX				56.31 322	74.23 326	51.81 330		56.67 341	44.85 328	48.54 327	75.16 319	27.87 331	58.74 338	40.92 329	52.22 326	58.39 330

PNet_i23d		50.48 309	47.18 309	60.36 320	68.59 331	44.56 336	72.75 330	72.61 336	43.92 329	33.91 332	60.19 330	6.16 341	73.52 334	38.50 331	28.04 331	63.01 327

Gipuma		57.99 305	54.91 306	67.24 318	88.51 291	65.59 311	52.21 334	90.33 289	43.58 330	42.84 329	51.18 332	20.29 337	85.07 327	34.77 332	70.45 309	51.05 331
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 307	48.46 308	63.48 319	45.72 340	46.20 333	73.41 327	78.31 333	41.03 331	30.06 333	65.68 326	6.05 342	83.43 329	30.04 333	65.86 316	60.80 328
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		50.55 308	44.13 310	69.81 314	56.77 337	54.58 328	73.22 328	80.78 329	39.79 332	22.08 337	46.69 334	4.03 344	79.71 332	47.65 322	26.13 332	75.14 324

E-PMN		43.23 311	42.29 311	46.03 323	65.58 334	37.41 338	73.51 326	64.62 337	33.99 333	28.47 335	47.87 333	19.90 338	67.91 335	22.23 335	24.45 333	32.77 332

EMVS		42.07 312	41.12 312	44.92 325	63.45 336	35.56 340	73.65 325	63.48 338	33.05 334	26.88 336	45.45 335	21.27 336	67.14 336	19.80 336	23.02 335	32.06 333

MVE	39.65 23	43.39 310	38.59 315	57.77 321	56.52 338	48.77 331	55.38 333	58.64 340	29.33 335	28.96 334	52.65 331	4.68 343	64.62 337	28.11 334	33.07 329	59.93 329
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		21.27 316	20.48 317	23.63 328	68.59 331	36.41 339	49.57 335	6.85 344	9.37 336	7.89 338	4.46 341	4.03 344	31.37 339	17.47 337	16.07 337	3.12 335

tmp_tt		35.64 314	39.24 313	24.84 327	14.87 341	23.90 342	62.71 332	51.51 342	6.58 337	36.66 331	62.08 329	44.37 320	30.34 340	52.40 314	22.00 336	20.27 334

testmvs		8.92 317	11.52 318	1.12 330	1.06 342	0.46 344	86.02 294	0.65 345	0.62 338	2.74 339	9.52 339	0.31 347	0.45 342	2.38 338	0.39 338	2.46 337

test123		8.76 318	11.22 319	1.39 329	0.85 343	0.97 343	85.76 298	0.35 346	0.54 339	2.45 340	8.14 340	0.60 346	0.48 341	2.16 339	0.17 340	2.71 336

cdsmvs_eth3d_5k		22.14 315	29.52 316	0.00 331	0.00 344	0.00 345	0.00 336	95.76 116	0.00 340	0.00 341	94.29 111	75.66 140	0.00 343	0.00 340	0.00 341	0.00 339

pcd_1.5k_mvsjas		6.64 320	8.86 321	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	79.70 88	0.00 343	0.00 340	0.00 341	0.00 339

pcd1.5k->3k		37.02 313	38.84 314	31.53 326	92.33 190	0.00 345	0.00 336	96.13 91	0.00 340	0.00 341	0.00 342	72.70 179	0.00 343	0.00 340	88.43 183	94.60 165

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

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

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

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

ab-mvs-re		7.82 319	10.43 320	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	93.88 129	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

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

ESAPD								97.46 6

sam_mvs1												71.70 189

sam_mvs												70.60 204

ambc				83.06 295	79.99 320	63.51 315	77.47 324	92.86 230		74.34 293	84.45 298	28.74 329	95.06 281	73.06 254	68.89 314	90.61 291

MTGPA								96.97 33

test_post1							88.00 282				9.81 338	69.31 223	95.53 260	76.65 226

test_post											10.29 337	70.57 208	95.91 249

patchmatchnet-post											83.76 301	71.53 192	96.48 225

GG-mvs-BLEND				87.94 249	89.73 282	77.91 232	87.80 283	78.23 334		80.58 245	83.86 300	59.88 286	95.33 276	71.20 261	92.22 137	90.60 293

MTMP								60.64 339

test9_res														91.91 40	98.71 17	98.07 43

agg_prior2														90.54 59	98.68 22	98.27 29

agg_prior					97.38 41	85.92 42		96.72 55		92.16 54			98.97 59

test_prior4						85.96 41	94.11 138

test_prior				93.82 50	97.29 46	84.49 62		96.88 42					98.87 65			98.11 41

新几何2							93.11 196

旧先验1					96.79 57	81.81 125		95.67 121			96.81 31	86.69 22			97.66 54	96.97 86

原ACMM2							92.94 204

testdata2													98.75 76	78.30 210

segment_acmp												87.16 19

test12				94.34 39	97.13 51	86.15 38		96.29 80		91.04 73		85.08 39	99.01 53		98.13 44	97.86 57

plane_prior7					94.70 123	82.74 109

plane_prior6					94.52 129	82.75 107						74.23 156

plane_prior5								96.22 85					98.12 106	88.15 77	89.99 157	94.63 162

plane_prior4											94.86 95

plane_prior1					94.59 127

n2								0.00 347

nn								0.00 347

door-mid								85.49 320

lessismore_v0				86.04 277	88.46 293	68.78 303		80.59 330		73.01 299	90.11 241	55.39 299	96.43 229	75.06 240	65.06 317	92.90 240

test11								96.57 69

door								85.33 321

HQP5-MVS						81.56 127

BP-MVS														87.11 95

HQP4-MVS										85.43 166			97.96 124			94.51 172

HQP3-MVS								96.04 98							89.77 162

HQP2-MVS												73.83 165

NP-MVS					94.37 135	82.42 117					93.98 122

ACMMP++_ref															87.47 193

ACMMP++															88.01 190

Test By Simon												80.02 83

High-resolution multi-view benchmark