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.

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

FC-MVSNet-test		90.27 86	90.18 78	90.53 152	93.71 161	79.85 173	95.77 43	97.59 2	89.31 40	86.27 137	94.67 101	81.93 70	97.01 203	84.26 124	88.09 190	94.71 161

FIs		90.51 83	90.35 74	90.99 143	93.99 151	80.98 146	95.73 44	97.54 3	89.15 44	86.72 128	94.68 100	81.83 71	97.24 186	85.18 109	88.31 187	94.76 160

PHI-MVS		93.89 30	93.65 32	94.62 29	96.84 56	86.43 29	96.69 21	97.49 4	85.15 127	93.56 27	96.28 53	85.60 33	99.31 26	92.45 23	98.79 9	98.12 40

UniMVSNet (Re)		89.80 97	89.07 99	92.01 103	93.60 164	84.52 61	94.78 95	97.47 5	89.26 41	86.44 134	92.32 177	82.10 65	97.39 174	84.81 115	80.84 265	94.12 186

ESAPD								97.46 6

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
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

CSCG		93.23 47	93.05 41	93.76 54	98.04 27	84.07 75	96.22 28	97.37 9	84.15 147	90.05 82	95.66 77	87.77 11	99.15 36	89.91 63	98.27 40	98.07 43

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

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

#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

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	97.76 134	92.19 31	98.66 26	96.76 92

VPA-MVSNet		89.62 99	88.96 101	91.60 122	93.86 155	82.89 105	95.46 55	97.33 13	87.91 71	88.43 97	93.31 141	74.17 159	97.40 171	87.32 91	82.86 235	94.52 171

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

WR-MVS_H		87.80 154	87.37 136	89.10 217	93.23 173	78.12 229	95.61 52	97.30 16	87.90 72	83.72 205	92.01 190	79.65 92	96.01 245	76.36 227	80.54 268	93.16 235

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.
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

MVS_111021_HR		93.45 37	93.31 36	93.84 49	96.99 53	84.84 54	93.24 193	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

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

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	86.13 179	94.85 15	98.54 5	86.60 24	96.93 12	97.19 21	90.66 22	92.85 34	23.41 337	85.02 41	99.49 14	91.99 36	98.56 33	98.47 12

MP-MVS-pluss		94.21 23	94.00 25	94.85 15	98.17 22	86.65 22	94.82 92	97.17 23	86.26 106	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
DELS-MVS		93.43 39	93.25 37	93.97 45	95.42 99	85.04 53	93.06 200	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
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

UniMVSNet_NR-MVSNet		89.92 95	89.29 94	91.81 117	93.39 168	83.72 81	94.43 117	97.12 25	89.80 31	86.46 131	93.32 140	83.16 53	97.23 188	84.92 112	81.02 261	94.49 175

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	96.91 210	94.38 5	98.85 6	98.03 47

DeepPCF-MVS	89.96 1	94.20 24	94.77 8	92.49 87	96.52 64	80.00 169	94.00 153	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

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 96	92.62 45	96.80 33	84.85 44	99.17 33	92.43 24	98.65 28	98.33 22

3Dnovator	86.66 5	91.73 62	90.82 70	94.44 34	94.59 128	86.37 30	97.18 6	97.02 30	89.20 42	84.31 195	96.66 39	73.74 167	99.17 33	86.74 98	97.96 48	97.79 61

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
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

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

MTGPA								96.97 33

MTAPA		94.42 16	94.22 16	95.00 8	98.42 12	86.95 10	94.36 129	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

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

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

APD-MVS		94.24 21	94.07 23	94.75 24	98.06 26	86.90 13	95.88 39	96.94 38	85.68 116	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
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

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

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
test_prior3		93.60 35	93.53 34	93.82 50	97.29 46	84.49 62	94.12 137	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_prior				93.82 50	97.29 46	84.49 62		96.88 42					98.87 65			98.11 41

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

PVSNet_BlendedMVS		89.98 91	89.70 85	90.82 146	96.12 76	81.25 137	93.92 156	96.83 45	83.49 163	89.10 90	92.26 179	81.04 76	98.85 71	86.72 101	87.86 192	92.35 259

PVSNet_Blended		90.73 77	90.32 75	91.98 106	96.12 76	81.25 137	92.55 216	96.83 45	82.04 200	89.10 90	92.56 170	81.04 76	98.85 71	86.72 101	95.91 79	95.84 119

原ACMM1				92.01 103	97.34 43	81.05 144		96.81 47	78.89 236	90.45 77	95.92 68	82.65 57	98.84 73	80.68 173	98.26 41	96.14 106

HPM-MVS_fast		93.40 40	93.22 38	93.94 47	98.36 16	84.83 55	97.15 7	96.80 48	85.77 113	92.47 49	97.13 22	82.38 59	99.07 42	90.51 60	98.40 37	97.92 55

TEST9					97.53 34	86.49 27	94.07 145	96.78 49	81.61 212	92.77 38	96.20 57	87.71 13	99.12 39

train_agg		93.44 38	93.08 40	94.52 32	97.53 34	86.49 27	94.07 145	96.78 49	81.86 207	92.77 38	96.20 57	87.63 14	99.12 39	92.14 33	98.69 19	97.94 51

3Dnovator+	87.14 4	92.42 55	91.37 59	95.55 2	95.63 94	88.73 2	97.07 8	96.77 51	90.84 17	84.02 199	96.62 41	75.95 133	99.34 21	87.77 83	97.68 53	98.59 7

test_8					97.49 37	86.30 35	94.02 151	96.76 52	81.86 207	92.70 42	96.20 57	87.63 14	99.02 51

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

agg_prior3		93.27 43	92.89 46	94.40 38	97.49 37	86.12 39	94.07 145	96.73 53	81.46 215	92.46 50	96.05 65	86.90 21	99.15 36	92.14 33	98.69 19	97.94 51

agg_prior1		93.29 42	92.97 44	94.26 41	97.38 41	85.92 42	93.92 156	96.72 55	81.96 201	92.16 54	96.23 55	87.85 10	98.97 59	91.95 39	98.55 35	97.90 56

agg_prior					97.38 41	85.92 42		96.72 55		92.16 54			98.97 59

Regformer-2		94.33 18	94.22 16	94.68 26	95.54 96	86.75 18	94.57 109	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

QAPM		89.51 103	88.15 123	93.59 56	94.92 115	84.58 59	96.82 18	96.70 57	78.43 244	83.41 213	96.19 60	73.18 174	99.30 27	77.11 223	96.54 73	96.89 90

CANet		93.54 36	93.20 39	94.55 31	95.65 93	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

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

CDPH-MVS		92.83 51	92.30 53	94.44 34	97.79 31	86.11 40	94.06 148	96.66 61	80.09 226	92.77 38	96.63 40	86.62 23	99.04 47	87.40 88	98.66 26	98.17 35

PVSNet_Blended_VisFu		91.38 67	90.91 68	92.80 77	96.39 66	83.17 95	94.87 90	96.66 61	83.29 169	89.27 88	94.46 106	80.29 81	99.17 33	87.57 86	95.37 88	96.05 112

DP-MVS Recon		91.95 58	91.28 61	93.96 46	98.33 17	85.92 42	94.66 105	96.66 61	82.69 192	90.03 83	95.82 72	82.30 61	99.03 48	84.57 118	96.48 75	96.91 88

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

PS-CasMVS		87.32 175	86.88 152	88.63 225	92.99 182	76.33 254	95.33 56	96.61 65	88.22 66	83.30 215	93.07 152	73.03 176	95.79 255	78.36 209	81.00 263	93.75 210

MVS_0304		93.25 45	92.62 49	95.14 7	95.72 91	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

DU-MVS		89.34 113	88.50 111	91.85 113	93.04 179	83.72 81	94.47 114	96.59 66	89.50 36	86.46 131	93.29 143	77.25 111	97.23 188	84.92 112	81.02 261	94.59 166

CP-MVSNet		87.63 161	87.26 140	88.74 222	93.12 176	76.59 251	95.29 61	96.58 68	88.43 61	83.49 212	92.98 158	75.28 145	95.83 252	78.97 204	81.15 258	93.79 204

test11								96.57 69

CPTT-MVS		91.99 57	91.80 56	92.55 84	98.24 21	81.98 124	96.76 19	96.49 70	81.89 206	90.24 79	96.44 49	78.59 99	98.61 83	89.68 64	97.85 51	97.06 84

Regformer-1		94.22 22	94.13 21	94.51 33	95.54 96	86.36 31	94.57 109	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

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

OpenMVS	83.78 11	88.74 127	87.29 138	93.08 66	92.70 186	85.39 50	96.57 22	96.43 73	78.74 241	80.85 242	96.07 64	69.64 218	99.01 53	78.01 214	96.65 70	94.83 157

canonicalmvs		93.27 43	92.75 48	94.85 15	95.70 92	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

Regformer-4		93.91 29	93.81 27	94.19 43	95.36 100	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

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

PEN-MVS		86.80 188	86.27 177	88.40 238	92.32 192	75.71 258	95.18 72	96.38 77	87.97 69	82.82 219	93.15 148	73.39 172	95.92 248	76.15 231	79.03 282	93.59 221

114514_t		89.51 103	88.50 111	92.54 85	98.11 23	81.99 123	95.16 74	96.36 78	70.19 305	85.81 143	95.25 85	76.70 116	98.63 81	82.07 152	96.86 66	97.00 85

TranMVSNet+NR-MVSNet		88.84 124	87.95 127	91.49 124	92.68 187	83.01 101	94.92 86	96.31 79	89.88 30	85.53 158	93.85 131	76.63 118	96.96 207	81.91 156	79.87 279	94.50 173

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

nrg030		91.08 73	90.39 73	93.17 63	93.07 177	86.91 12	96.41 24	96.26 81	88.30 64	88.37 98	94.85 97	82.19 64	97.64 141	91.09 51	82.95 233	94.96 146

无先验							93.28 190	96.26 81	73.95 280				99.05 44	80.56 175		96.59 96

NR-MVSNet		88.58 130	87.47 134	91.93 109	93.04 179	84.16 74	94.77 96	96.25 83	89.05 45	80.04 254	93.29 143	79.02 94	97.05 201	81.71 160	80.05 274	94.59 166

PAPM_NR		91.22 70	90.78 71	92.52 86	97.60 33	81.46 132	94.37 125	96.24 84	86.39 104	87.41 115	94.80 99	82.06 67	98.48 89	82.80 141	95.37 88	97.61 65

HQP_MVS		90.60 82	90.19 77	91.82 115	94.70 124	82.73 110	95.85 40	96.22 85	90.81 18	86.91 124	94.86 95	74.23 156	98.12 106	88.15 77	89.99 158	94.63 162

plane_prior5								96.22 85					98.12 106	88.15 77	89.99 158	94.63 162

PAPR		90.02 90	89.27 96	92.29 96	95.78 89	80.95 148	92.68 211	96.22 85	81.91 204	86.66 129	93.75 135	82.23 62	98.44 92	79.40 202	94.79 94	97.48 70

TAPA-MVS	84.62 6	88.16 139	87.01 150	91.62 121	96.64 59	80.65 154	94.39 121	96.21 88	76.38 259	86.19 139	95.44 80	79.75 86	98.08 118	62.75 303	95.29 90	96.13 107
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LPG-MVS_test		89.45 106	88.90 104	91.12 134	94.47 132	81.49 130	95.30 59	96.14 89	86.73 98	85.45 164	95.16 87	69.89 214	98.10 112	87.70 84	89.23 172	93.77 208

LGP-MVS_train				91.12 134	94.47 132	81.49 130		96.14 89	86.73 98	85.45 164	95.16 87	69.89 214	98.10 112	87.70 84	89.23 172	93.77 208

pcd1.5k->3k		37.02 314	38.84 315	31.53 327	92.33 191	0.00 346	0.00 337	96.13 91	0.00 341	0.00 342	0.00 343	72.70 179	0.00 344	0.00 341	88.43 184	94.60 165

ACMM	84.12 9	89.14 115	88.48 114	91.12 134	94.65 127	81.22 139	95.31 57	96.12 92	85.31 123	85.92 142	94.34 107	70.19 213	98.06 120	85.65 106	88.86 177	94.08 190
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS_111021_LR		92.47 54	92.29 54	92.98 71	95.99 83	84.43 69	93.08 198	96.09 93	88.20 67	91.12 72	95.72 76	81.33 74	97.76 134	91.74 44	97.37 59	96.75 93

CLD-MVS		89.47 105	88.90 104	91.18 133	94.22 140	82.07 122	92.13 229	96.09 93	87.90 72	85.37 173	92.45 172	74.38 154	97.56 144	87.15 93	90.43 153	93.93 194
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
alignmvs		93.08 49	92.50 52	94.81 20	95.62 95	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

XVG-OURS		89.40 111	88.70 107	91.52 123	94.06 144	81.46 132	91.27 246	96.07 95	86.14 109	88.89 93	95.77 74	68.73 234	97.26 184	87.39 89	89.96 160	95.83 120

XVG-OURS-SEG-HR		89.95 93	89.45 89	91.47 125	94.00 150	81.21 140	91.87 233	96.06 97	85.78 112	88.55 95	95.73 75	74.67 152	97.27 182	88.71 72	89.64 165	95.91 115

HQP3-MVS								96.04 98							89.77 163

HQP-MVS		89.80 97	89.28 95	91.34 128	94.17 141	81.56 127	94.39 121	96.04 98	88.81 50	85.43 167	93.97 123	73.83 165	97.96 125	87.11 95	89.77 163	94.50 173

PS-MVSNAJss		89.97 92	89.62 86	91.02 141	91.90 198	80.85 151	95.26 68	95.98 100	86.26 106	86.21 138	94.29 111	79.70 88	97.65 139	88.87 71	88.10 188	94.57 168

Vis-MVSNet		91.75 61	91.23 62	93.29 58	95.32 103	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
WR-MVS		88.38 132	87.67 131	90.52 158	93.30 171	80.18 162	93.26 191	95.96 102	88.57 59	85.47 163	92.81 164	76.12 122	96.91 210	81.24 163	82.29 239	94.47 177

OMC-MVS		91.23 69	90.62 72	93.08 66	96.27 69	84.07 75	93.52 179	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

v7n		86.81 187	85.76 189	89.95 191	90.72 260	79.25 201	95.07 78	95.92 104	84.45 142	82.29 223	90.86 227	72.60 182	97.53 146	79.42 201	80.52 270	93.08 239

AdaColmap		89.89 96	89.07 99	92.37 93	97.41 40	83.03 99	94.42 118	95.92 104	82.81 188	86.34 136	94.65 102	73.89 163	99.02 51	80.69 172	95.51 84	95.05 140

cascas		86.43 198	84.98 203	90.80 147	92.10 196	80.92 149	90.24 254	95.91 106	73.10 286	83.57 210	88.39 265	65.15 259	97.46 151	84.90 114	91.43 140	94.03 192

MVSFormer		91.68 64	91.30 60	92.80 77	93.86 155	83.88 78	95.96 36	95.90 107	84.66 136	91.76 63	94.91 92	77.92 107	97.30 178	89.64 65	97.11 61	97.24 74

test_djsdf		89.03 120	88.64 108	90.21 170	90.74 259	79.28 199	95.96 36	95.90 107	84.66 136	85.33 174	92.94 159	74.02 162	97.30 178	89.64 65	88.53 180	94.05 191

ACMP	84.23 8	89.01 122	88.35 115	90.99 143	94.73 121	81.27 136	95.07 78	95.89 109	86.48 101	83.67 207	94.30 110	69.33 221	97.99 124	87.10 97	88.55 179	93.72 212
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PCF-MVS	84.11 10	87.74 156	86.08 182	92.70 80	94.02 146	84.43 69	89.27 269	95.87 110	73.62 282	84.43 189	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
CHOSEN 1792x2688		88.84 124	87.69 130	92.30 95	96.14 75	81.42 134	90.01 258	95.86 111	74.52 277	87.41 115	93.94 124	75.46 143	98.36 93	80.36 179	95.53 83	97.12 82

Regformer-3		93.68 33	93.64 33	93.81 52	95.36 100	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

MVS_Test		91.31 68	91.11 63	91.93 109	94.37 136	80.14 164	93.46 182	95.80 113	86.46 102	91.35 69	93.77 133	82.21 63	98.09 117	87.57 86	94.95 93	97.55 69

HyFIR lowres test		88.09 142	86.81 156	91.93 109	96.00 82	80.63 155	90.01 258	95.79 114	73.42 283	87.68 113	92.10 185	73.86 164	97.96 125	80.75 171	91.70 138	97.19 78

EI-MVSNet-Vis-set		93.01 50	92.92 45	93.29 58	95.01 110	83.51 88	94.48 112	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

cdsmvs_eth3d_5k		22.14 316	29.52 317	0.00 332	0.00 345	0.00 346	0.00 337	95.76 116	0.00 341	0.00 342	94.29 111	75.66 140	0.00 344	0.00 341	0.00 342	0.00 340

DTE-MVSNet		86.11 201	85.48 195	87.98 248	91.65 208	74.92 261	94.93 85	95.75 117	87.36 84	82.26 224	93.04 153	72.85 177	95.82 253	74.04 247	77.46 287	93.20 233

OPM-MVS		90.12 88	89.56 87	91.82 115	93.14 175	83.90 77	94.16 136	95.74 118	88.96 49	87.86 103	95.43 81	72.48 184	97.91 129	88.10 80	90.18 157	93.65 214

EI-MVSNet-UG-set		92.74 53	92.62 49	93.12 64	94.86 118	83.20 94	94.40 119	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

PS-MVSNAJ		91.18 71	90.92 67	91.96 107	95.26 106	82.60 116	92.09 231	95.70 120	86.27 105	91.84 60	92.46 171	79.70 88	98.99 57	89.08 68	95.86 80	94.29 180

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

MAR-MVS		90.30 85	89.37 92	93.07 68	96.61 60	84.48 64	95.68 47	95.67 121	82.36 195	87.85 104	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
mvs_tets		88.06 143	87.28 139	90.38 166	90.94 250	79.88 171	95.22 70	95.66 123	85.10 128	84.21 198	93.94 124	63.53 266	97.40 171	88.50 74	88.40 186	93.87 199

MVS		87.44 172	86.10 181	91.44 126	92.61 188	83.62 85	92.63 212	95.66 123	67.26 313	81.47 235	92.15 181	77.95 106	98.22 101	79.71 194	95.48 85	92.47 254

jajsoiax		88.24 137	87.50 132	90.48 161	90.89 254	80.14 164	95.31 57	95.65 125	84.97 130	84.24 197	94.02 120	65.31 258	97.42 164	88.56 73	88.52 181	93.89 196

xiu_mvs_v2_base		91.13 72	90.89 69	91.86 112	94.97 113	82.42 117	92.24 225	95.64 126	86.11 110	91.74 65	93.14 149	79.67 91	98.89 64	89.06 69	95.46 87	94.28 181

ab-mvs		89.41 109	88.35 115	92.60 82	95.15 108	82.65 114	92.20 227	95.60 127	83.97 149	88.55 95	93.70 136	74.16 160	98.21 102	82.46 147	89.37 168	96.94 87

testing_2		83.40 248	81.02 253	90.56 151	85.06 309	80.51 159	91.37 244	95.57 128	82.92 185	67.06 314	85.54 297	49.47 313	97.24 186	86.74 98	85.44 209	93.93 194

新几何1				93.10 65	97.30 45	84.35 71		95.56 129	71.09 302	91.26 70	96.24 54	82.87 56	98.86 68	79.19 203	98.10 45	96.07 110

anonymousdsp		87.84 150	87.09 145	90.12 178	89.13 286	80.54 158	94.67 104	95.55 130	82.05 199	83.82 203	92.12 182	71.47 194	97.15 192	87.15 93	87.80 193	92.67 248

XVG-ACMP-BASELINE		86.00 203	84.84 208	89.45 208	91.20 237	78.00 231	91.70 238	95.55 130	85.05 129	82.97 217	92.25 180	54.49 303	97.48 149	82.93 138	87.45 195	92.89 242

v52		86.50 195	85.53 194	89.39 209	89.17 285	78.99 208	94.72 100	95.54 132	83.59 157	82.10 227	90.60 233	71.59 191	97.45 153	82.52 143	79.99 276	91.73 269

V4		86.50 195	85.54 191	89.39 209	89.13 286	78.99 208	94.73 97	95.54 132	83.59 157	82.10 227	90.61 232	71.60 190	97.45 153	82.52 143	80.01 275	91.74 268

VPNet		88.20 138	87.47 134	90.39 164	93.56 165	79.46 183	94.04 149	95.54 132	88.67 55	86.96 122	94.58 105	69.33 221	97.15 192	84.05 128	80.53 269	94.56 169

Test4		85.75 210	83.72 224	91.83 114	88.08 298	81.03 145	92.48 217	95.54 132	83.38 167	73.40 298	88.57 262	50.99 310	97.37 175	86.61 103	94.47 103	97.09 83

1121		90.42 84	89.49 88	93.20 61	97.27 48	84.46 65	92.63 212	95.51 136	71.01 303	91.20 71	96.21 56	82.92 55	99.05 44	80.56 175	98.07 46	96.10 108

v748		86.27 199	85.28 199	89.25 213	90.26 272	77.58 244	94.89 87	95.50 137	84.28 146	81.41 237	90.46 237	72.57 183	97.32 177	79.81 193	78.36 283	92.84 244

v1192		87.25 178	86.33 174	90.00 190	90.76 258	79.04 207	93.80 162	95.48 138	82.57 193	85.48 162	91.18 219	73.38 173	97.42 164	82.30 149	82.06 242	93.53 223

xiu_mvs_v1_base_debu		90.64 79	90.05 81	92.40 90	93.97 152	84.46 65	93.32 184	95.46 139	85.17 124	92.25 51	94.03 117	70.59 205	98.57 85	90.97 52	94.67 95	94.18 182

xiu_mvs_v1_base		90.64 79	90.05 81	92.40 90	93.97 152	84.46 65	93.32 184	95.46 139	85.17 124	92.25 51	94.03 117	70.59 205	98.57 85	90.97 52	94.67 95	94.18 182

xiu_mvs_v1_base_debi		90.64 79	90.05 81	92.40 90	93.97 152	84.46 65	93.32 184	95.46 139	85.17 124	92.25 51	94.03 117	70.59 205	98.57 85	90.97 52	94.67 95	94.18 182

v7		87.75 155	86.96 151	90.12 178	91.20 237	79.50 178	94.28 131	95.46 139	83.45 164	85.75 147	91.56 206	75.13 146	97.43 162	83.60 132	82.18 241	93.42 228

v10		87.25 178	86.38 172	89.85 193	91.19 239	79.50 178	94.48 112	95.45 143	83.79 154	83.62 208	91.19 218	75.13 146	97.42 164	81.94 155	80.60 267	92.63 250

F-COLMAP		87.95 147	86.80 157	91.40 127	96.35 68	80.88 150	94.73 97	95.45 143	79.65 231	82.04 230	94.61 103	71.13 196	98.50 88	76.24 230	91.05 145	94.80 159

PLC	84.53 7	89.06 119	88.03 125	92.15 100	97.27 48	82.69 113	94.29 130	95.44 145	79.71 230	84.01 200	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
v144192		87.19 182	86.35 173	89.74 198	90.64 263	78.24 227	93.92 156	95.43 146	81.93 203	85.51 160	91.05 225	74.21 158	97.45 153	82.86 139	81.56 255	93.53 223

v1921920		86.97 186	86.06 183	89.69 201	90.53 268	78.11 230	93.80 162	95.43 146	81.90 205	85.33 174	91.05 225	72.66 180	97.41 169	82.05 153	81.80 250	93.53 223

v1144		87.61 167	86.79 158	90.06 186	91.01 245	79.34 195	93.95 155	95.42 148	83.36 168	85.66 154	91.31 214	74.98 150	97.42 164	83.37 133	82.06 242	93.42 228

v8		87.50 171	86.71 161	89.89 192	91.37 221	79.40 191	94.50 111	95.38 149	84.81 133	83.60 209	91.33 211	76.05 126	97.42 164	82.84 140	80.51 271	92.84 244

sss		88.93 123	88.26 122	90.94 145	94.05 145	80.78 153	91.71 237	95.38 149	81.55 213	88.63 94	93.91 128	75.04 149	95.47 267	82.47 146	91.61 139	96.57 97

v1240		86.78 189	85.85 187	89.56 203	90.45 269	77.79 238	93.61 176	95.37 151	81.65 209	85.43 167	91.15 221	71.50 193	97.43 162	81.47 162	82.05 244	93.47 227

testdata				90.49 160	96.40 65	77.89 235		95.37 151	72.51 292	93.63 23	96.69 36	82.08 66	97.65 139	83.08 135	97.39 58	95.94 114

1314		87.51 170	86.57 170	90.34 168	92.42 190	79.74 176	92.63 212	95.35 153	78.35 245	80.14 252	91.62 202	74.05 161	97.15 192	81.05 164	93.53 116	94.12 186

v1neww		87.98 144	87.25 141	90.16 172	91.38 219	79.41 187	94.37 125	95.28 154	84.48 139	85.77 145	91.53 207	76.12 122	97.45 153	84.45 121	81.89 246	93.61 219

v7new		87.98 144	87.25 141	90.16 172	91.38 219	79.41 187	94.37 125	95.28 154	84.48 139	85.77 145	91.53 207	76.12 122	97.45 153	84.45 121	81.89 246	93.61 219

V42		87.68 157	86.86 153	90.15 176	90.58 264	80.14 164	94.24 133	95.28 154	83.66 156	85.67 153	91.33 211	74.73 151	97.41 169	84.43 123	81.83 249	92.89 242

v6		87.98 144	87.25 141	90.16 172	91.36 222	79.39 192	94.37 125	95.27 157	84.48 139	85.78 144	91.51 209	76.15 121	97.46 151	84.46 120	81.88 248	93.62 218

EPP-MVSNet		91.70 63	91.56 58	92.13 102	95.88 86	80.50 160	97.33 3	95.25 158	86.15 108	89.76 84	95.60 78	83.42 52	98.32 98	87.37 90	93.25 124	97.56 68

v1141		87.84 150	87.09 145	90.11 183	91.23 234	79.25 201	94.08 143	95.24 159	84.44 143	85.69 152	91.31 214	75.91 134	97.44 160	84.17 126	81.74 252	93.63 217

divwei89l23v2f112		87.84 150	87.09 145	90.10 185	91.23 234	79.24 203	94.09 141	95.24 159	84.44 143	85.70 150	91.31 214	75.91 134	97.44 160	84.17 126	81.73 253	93.64 215

v1		87.85 149	87.10 144	90.11 183	91.21 236	79.24 203	94.09 141	95.24 159	84.44 143	85.70 150	91.31 214	75.96 132	97.45 153	84.18 125	81.73 253	93.64 215

UGNet		89.95 93	88.95 102	92.95 72	94.51 131	83.31 92	95.70 46	95.23 162	89.37 39	87.58 114	93.94 124	64.00 264	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
XXY-MVS		87.65 158	86.85 154	90.03 187	92.14 194	80.60 157	93.76 165	95.23 162	82.94 184	84.60 183	94.02 120	74.27 155	95.49 266	81.04 165	83.68 226	94.01 193

API-MVS		90.66 78	90.07 80	92.45 89	96.36 67	84.57 60	96.06 33	95.22 164	82.39 194	89.13 89	94.27 114	80.32 80	98.46 90	80.16 184	96.71 68	94.33 179

MG-MVS		91.77 60	91.70 57	92.00 105	97.08 52	80.03 168	93.60 177	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

v2v482		87.84 150	87.06 148	90.17 171	90.99 246	79.23 205	94.00 153	95.13 166	84.87 131	85.53 158	92.07 188	74.45 153	97.45 153	84.71 117	81.75 251	93.85 202

Effi-MVS+		91.59 65	91.11 63	93.01 70	94.35 139	83.39 91	94.60 106	95.10 167	87.10 86	90.57 76	93.10 151	81.43 73	98.07 119	89.29 67	94.48 102	97.59 66

Fast-Effi-MVS+		89.41 109	88.64 108	91.71 119	94.74 120	80.81 152	93.54 178	95.10 167	83.11 172	86.82 127	90.67 230	79.74 87	97.75 137	80.51 177	93.55 115	96.57 97

IterMVS-LS		88.36 134	87.91 129	89.70 200	93.80 158	78.29 226	93.73 168	95.08 169	85.73 114	84.75 181	91.90 193	79.88 84	96.92 209	83.83 130	82.51 237	93.89 196
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test222					96.55 63	81.70 126	92.22 226	95.01 170	68.36 309	90.20 80	96.14 62	80.26 82			97.80 52	96.05 112

EI-MVSNet		89.10 116	88.86 106	89.80 197	91.84 200	78.30 225	93.70 172	95.01 170	85.73 114	87.15 119	95.28 83	79.87 85	97.21 190	83.81 131	87.36 196	93.88 198

MVSTER		88.84 124	88.29 120	90.51 159	92.95 183	80.44 161	93.73 168	95.01 170	84.66 136	87.15 119	93.12 150	72.79 178	97.21 190	87.86 82	87.36 196	93.87 199

diffmvs		89.07 117	88.32 118	91.34 128	93.24 172	79.79 174	92.29 224	94.98 173	80.24 223	87.38 118	92.45 172	78.02 105	97.33 176	83.29 134	92.93 130	96.91 88

GBi-Net		87.26 176	85.98 184	91.08 137	94.01 147	83.10 96	95.14 75	94.94 174	83.57 159	84.37 190	91.64 198	66.59 248	96.34 234	78.23 211	85.36 210	93.79 204

test1		87.26 176	85.98 184	91.08 137	94.01 147	83.10 96	95.14 75	94.94 174	83.57 159	84.37 190	91.64 198	66.59 248	96.34 234	78.23 211	85.36 210	93.79 204

FMVSNet2		87.19 182	85.82 188	91.30 130	94.01 147	83.67 83	94.79 94	94.94 174	83.57 159	83.88 201	92.05 189	66.59 248	96.51 224	77.56 218	85.01 214	93.73 211

FMVSNet1		85.85 206	84.11 216	91.08 137	92.81 185	83.10 96	95.14 75	94.94 174	81.64 210	82.68 220	91.64 198	59.01 290	96.34 234	75.37 236	83.78 223	93.79 204

LS3D		87.89 148	86.32 175	92.59 83	96.07 80	82.92 104	95.23 69	94.92 178	75.66 266	82.89 218	95.98 66	72.48 184	99.21 30	68.43 281	95.23 92	95.64 127

LTVRE_ROB	82.13 13	86.26 200	84.90 206	90.34 168	94.44 135	81.50 129	92.31 223	94.89 179	83.03 179	79.63 257	92.67 167	69.69 217	97.79 132	71.20 261	86.26 204	91.72 270
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
UnsupCasMVSNet_eth		80.07 274	78.27 276	85.46 283	85.24 308	72.63 280	88.45 280	94.87 180	82.99 183	71.64 306	88.07 270	56.34 297	91.75 310	73.48 252	63.36 323	92.01 265

pm-mvs1		86.61 193	85.54 191	89.82 194	91.44 212	80.18 162	95.28 67	94.85 181	83.84 151	81.66 234	92.62 169	72.45 186	96.48 226	79.67 195	78.06 284	92.82 246

ACMH	80.38 17	85.36 214	83.68 226	90.39 164	94.45 134	80.63 155	94.73 97	94.85 181	82.09 198	77.24 271	92.65 168	60.01 286	97.58 142	72.25 257	84.87 215	92.96 240
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvs_anonymous		89.37 112	89.32 93	89.51 207	93.47 166	74.22 263	91.65 240	94.83 183	82.91 186	85.45 164	93.79 132	81.23 75	96.36 233	86.47 104	94.09 108	97.94 51

FMVSNet3		87.40 174	86.11 180	91.30 130	93.79 160	83.64 84	94.20 135	94.81 184	83.89 150	84.37 190	91.87 194	68.45 239	96.56 221	78.23 211	85.36 210	93.70 213

WTY-MVS		89.60 100	88.92 103	91.67 120	95.47 98	81.15 142	92.38 221	94.78 185	83.11 172	89.06 92	94.32 109	78.67 98	96.61 220	81.57 161	90.89 151	97.24 74

PAPM		86.68 192	85.39 197	90.53 152	93.05 178	79.33 198	89.79 262	94.77 186	78.82 238	81.95 231	93.24 145	76.81 114	97.30 178	66.94 287	93.16 126	94.95 153

CDS-MVSNet		89.45 106	88.51 110	92.29 96	93.62 163	83.61 86	93.01 201	94.68 187	81.95 202	87.82 110	93.24 145	78.69 97	96.99 204	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
test_normal		88.13 141	86.78 159	92.18 99	90.55 267	81.19 141	92.74 209	94.64 188	83.84 151	77.49 270	90.51 236	68.49 238	98.16 104	88.22 76	94.55 100	97.21 77

DI_MVS_plusplus_test		88.15 140	86.82 155	92.14 101	90.67 262	81.07 143	93.01 201	94.59 189	83.83 153	77.78 267	90.63 231	68.51 237	98.16 104	88.02 81	94.37 106	97.17 79

1112_ss		88.42 131	87.33 137	91.72 118	94.92 115	80.98 146	92.97 204	94.54 190	78.16 249	83.82 203	93.88 129	78.78 96	97.91 129	79.45 198	89.41 167	96.26 103

HY-MVS	83.01 12	89.03 120	87.94 128	92.29 96	94.86 118	82.77 106	92.08 232	94.49 191	81.52 214	86.93 123	92.79 166	78.32 104	98.23 100	79.93 188	90.55 152	95.88 117

CANet_DTU		90.26 87	89.41 91	92.81 76	93.46 167	83.01 101	93.48 180	94.47 192	89.43 37	87.76 112	94.23 115	70.54 209	99.03 48	84.97 111	96.39 76	96.38 100

v148		87.04 185	86.32 175	89.21 214	90.94 250	77.26 245	93.71 171	94.43 193	84.84 132	84.36 193	90.80 228	76.04 128	97.05 201	82.12 151	79.60 280	93.31 230

OurMVSNet-221017-0		85.35 215	84.64 213	87.49 258	90.77 257	72.59 281	94.01 152	94.40 194	84.72 135	79.62 258	93.17 147	61.91 273	96.72 214	81.99 154	81.16 256	93.16 235

Effi-MVS+-dtu		88.65 128	88.35 115	89.54 204	93.33 169	76.39 252	94.47 114	94.36 195	87.70 77	85.43 167	89.56 251	73.45 170	97.26 184	85.57 107	91.28 141	94.97 143

mvs-test1		89.45 106	89.14 97	90.38 166	93.33 169	77.63 243	94.95 83	94.36 195	87.70 77	87.10 121	92.81 164	73.45 170	98.03 122	85.57 107	93.04 128	95.48 130

EG-PatchMatch MVS		82.37 256	80.34 258	88.46 236	90.27 271	79.35 194	92.80 208	94.33 197	77.14 256	73.26 299	90.18 241	47.47 317	96.72 214	70.25 266	87.32 198	89.30 299

Test_1112_low_res		87.65 158	86.51 171	91.08 137	94.94 114	79.28 199	91.77 234	94.30 198	76.04 264	83.51 211	92.37 175	77.86 109	97.73 138	78.69 207	89.13 174	96.22 104

pmmvs6		83.42 246	81.60 249	88.87 219	88.01 299	77.87 236	94.96 82	94.24 199	74.67 276	78.80 261	91.09 224	60.17 285	96.49 225	77.06 225	75.40 292	92.23 262

MVP-Stereo		85.97 204	84.86 207	89.32 211	90.92 252	82.19 120	92.11 230	94.19 200	78.76 240	78.77 262	91.63 201	68.38 240	96.56 221	75.01 241	93.95 109	89.20 301
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TAMVS		89.21 114	88.29 120	91.96 107	93.71 161	82.62 115	93.30 188	94.19 200	82.22 196	87.78 111	93.94 124	78.83 95	96.95 208	77.70 216	92.98 129	96.32 101

jason		90.80 75	90.10 79	92.90 74	93.04 179	83.53 87	93.08 198	94.15 202	80.22 224	91.41 68	94.91 92	76.87 113	97.93 128	90.28 62	96.90 64	97.24 74
jason: jason.
BH-untuned		88.60 129	88.13 124	90.01 189	95.24 107	78.50 220	93.29 189	94.15 202	84.75 134	84.46 187	93.40 137	75.76 138	97.40 171	77.59 217	94.52 101	94.12 186

ACMH+	81.04 14	85.05 221	83.46 233	89.82 194	94.66 126	79.37 193	94.44 116	94.12 204	82.19 197	78.04 265	92.82 163	58.23 292	97.54 145	73.77 250	82.90 234	92.54 251

v18		84.97 223	83.76 221	88.60 228	91.36 222	79.41 187	93.82 161	94.04 205	83.00 182	76.61 274	86.60 283	76.19 120	95.43 268	80.39 178	71.79 301	90.96 282

v16		84.96 224	83.74 223	88.62 226	91.40 217	79.48 181	93.83 159	94.04 205	83.03 179	76.54 275	86.59 284	76.11 125	95.42 269	80.33 181	71.80 300	90.95 284

v17		84.93 226	83.70 225	88.62 226	91.36 222	79.48 181	93.83 159	94.03 207	83.04 178	76.51 276	86.57 285	76.05 126	95.42 269	80.31 183	71.65 302	90.96 282

v15		84.79 229	83.53 230	88.57 232	91.30 233	79.41 187	93.70 172	94.01 208	83.06 175	76.27 277	86.42 289	76.03 129	95.38 271	80.01 185	71.00 305	90.92 285

v12		84.74 232	83.46 233	88.58 229	91.32 229	79.50 178	93.75 166	94.01 208	83.06 175	75.98 285	86.41 290	75.82 137	95.36 274	79.87 191	70.89 309	90.89 288

V14		84.79 229	83.52 231	88.57 232	91.32 229	79.43 186	93.72 170	94.01 208	83.06 175	76.22 278	86.43 286	76.01 130	95.37 272	79.96 187	70.99 306	90.91 286

v13		84.72 234	83.44 235	88.58 229	91.31 232	79.52 177	93.77 164	94.00 211	83.03 179	75.85 286	86.38 291	75.84 136	95.35 275	79.83 192	70.95 307	90.87 289

V9		84.77 231	83.50 232	88.58 229	91.33 227	79.46 183	93.75 166	94.00 211	83.07 174	76.07 283	86.43 286	75.97 131	95.37 272	79.91 190	70.93 308	90.91 286

v11		84.67 236	83.41 236	88.44 237	91.32 229	79.13 206	93.69 175	93.99 213	82.81 188	76.20 279	86.24 293	75.48 142	95.35 275	79.53 196	71.48 304	90.85 290

Fast-Effi-MVS+-dtu		87.44 172	86.72 160	89.63 202	92.04 197	77.68 242	94.03 150	93.94 214	85.81 111	82.42 222	91.32 213	70.33 211	97.06 200	80.33 181	90.23 156	94.14 185

TSAR-MVS + GP.		93.66 34	93.41 35	94.41 37	96.59 61	86.78 16	94.40 119	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

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	62.09 271	98.64 80	90.95 55	92.62 134	97.93 54

lupinMVS		90.92 74	90.21 76	93.03 69	93.86 155	83.88 78	92.81 207	93.86 216	79.84 228	91.76 63	94.29 111	77.92 107	98.04 121	90.48 61	97.11 61	97.17 79

CMPMVS	59.16 21	80.52 272	79.20 270	84.48 290	83.98 312	67.63 308	89.95 260	93.84 218	64.79 318	66.81 315	91.14 222	57.93 294	95.17 278	76.25 229	88.10 188	90.65 291
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
GA-MVS		86.61 193	85.27 200	90.66 148	91.33 227	78.71 211	90.40 252	93.81 219	85.34 122	85.12 176	89.57 250	61.25 277	97.11 196	80.99 168	89.59 166	96.15 105

IS-MVSNet		91.43 66	91.09 65	92.46 88	95.87 88	81.38 135	96.95 9	93.69 220	89.72 34	89.50 87	95.98 66	78.57 100	97.77 133	83.02 137	96.50 74	98.22 33

MS-PatchMatch		85.05 221	84.16 215	87.73 252	91.42 216	78.51 219	91.25 247	93.53 221	77.50 251	80.15 251	91.58 203	61.99 272	95.51 263	75.69 233	94.35 107	89.16 302

BH-w/o		87.57 169	87.05 149	89.12 215	94.90 117	77.90 234	92.41 219	93.51 222	82.89 187	83.70 206	91.34 210	75.75 139	97.07 199	75.49 234	93.49 117	92.39 257

UnsupCasMVSNet_bld		76.23 287	73.27 289	85.09 287	83.79 313	72.92 273	85.65 301	93.47 223	71.52 297	68.84 310	79.08 318	49.77 311	93.21 299	66.81 291	60.52 325	89.13 304

USDC		82.76 251	81.26 252	87.26 262	91.17 240	74.55 262	89.27 269	93.39 224	78.26 247	75.30 288	92.08 186	54.43 304	96.63 217	71.64 258	85.79 208	90.61 292

CNLPA		89.07 117	87.98 126	92.34 94	96.87 55	84.78 56	94.08 143	93.24 225	81.41 216	84.46 187	95.13 89	75.57 141	96.62 218	77.21 221	93.84 112	95.61 128

VDDNet		89.56 102	88.49 113	92.76 79	95.07 109	82.09 121	96.30 26	93.19 226	81.05 220	91.88 59	96.86 28	61.16 280	98.33 97	88.43 75	92.49 135	97.84 58

MSDG		84.86 228	83.09 238	90.14 177	93.80 158	80.05 167	89.18 272	93.09 227	78.89 236	78.19 263	91.91 192	65.86 257	97.27 182	68.47 280	88.45 183	93.11 237

BH-RMVSNet		88.37 133	87.48 133	91.02 141	95.28 104	79.45 185	92.89 206	93.07 228	85.45 120	86.91 124	94.84 98	70.35 210	97.76 134	73.97 248	94.59 99	95.85 118

ITE_SJBPF				88.24 243	91.88 199	77.05 247		92.92 229	85.54 118	80.13 253	93.30 142	57.29 295	96.20 238	72.46 256	84.71 216	91.49 273

ambc				83.06 296	79.99 321	63.51 316	77.47 325	92.86 230		74.34 294	84.45 299	28.74 330	95.06 282	73.06 254	68.89 315	90.61 292

TR-MVS		86.78 189	85.76 189	89.82 194	94.37 136	78.41 222	92.47 218	92.83 231	81.11 219	86.36 135	92.40 174	68.73 234	97.48 149	73.75 251	89.85 162	93.57 222

TransMVSNet (Re)		84.43 238	83.06 239	88.54 234	91.72 204	78.44 221	95.18 72	92.82 232	82.73 190	79.67 256	92.12 182	73.49 169	95.96 247	71.10 264	68.73 316	91.21 278

CHOSEN 280x420		85.15 219	83.99 218	88.65 224	92.47 189	78.40 223	79.68 322	92.76 233	74.90 274	81.41 237	89.59 249	69.85 216	95.51 263	79.92 189	95.29 90	92.03 264

MIMVSNet1		79.38 279	77.28 279	85.69 281	86.35 305	73.67 270	91.61 241	92.75 234	78.11 250	72.64 302	88.12 269	48.16 315	91.97 309	60.32 307	77.49 286	91.43 275

PVSNet	78.82 18	85.55 212	84.65 212	88.23 244	94.72 122	71.93 284	87.12 290	92.75 234	78.80 239	84.95 179	90.53 235	64.43 263	96.71 216	74.74 242	93.86 111	96.06 111

pmmvs4		85.43 213	83.86 220	90.16 172	90.02 278	82.97 103	90.27 253	92.67 236	75.93 265	80.73 243	91.74 197	71.05 197	95.73 257	78.85 205	83.46 230	91.78 267

semantic-postprocess				88.18 245	91.71 205	76.87 249		92.65 237	85.40 121	81.44 236	90.54 234	66.21 252	95.00 283	81.04 165	81.05 259	92.66 249

Baseline_NR-MVSNet		87.07 184	86.63 169	88.40 238	91.44 212	77.87 236	94.23 134	92.57 238	84.12 148	85.74 149	92.08 186	77.25 111	96.04 242	82.29 150	79.94 277	91.30 277

RPSCF		85.07 220	84.27 214	87.48 259	92.91 184	70.62 296	91.69 239	92.46 239	76.20 263	82.67 221	95.22 86	63.94 265	97.29 181	77.51 219	85.80 207	94.53 170

IterMVS		84.88 227	83.98 219	87.60 254	91.44 212	76.03 256	90.18 256	92.41 240	83.24 171	81.06 241	90.42 238	66.60 247	94.28 288	79.46 197	80.98 264	92.48 253
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchMatch-RL		86.77 191	85.54 191	90.47 162	95.88 86	82.71 112	90.54 251	92.31 241	79.82 229	84.32 194	91.57 205	68.77 233	96.39 231	73.16 253	93.48 119	92.32 260

COLMAP_ROB	80.39 16	83.96 241	82.04 247	89.74 198	95.28 104	79.75 175	94.25 132	92.28 242	75.17 270	78.02 266	93.77 133	58.60 291	97.84 131	65.06 296	85.92 205	91.63 271
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FMVSNet5		81.52 263	79.60 267	87.27 261	91.17 240	77.95 232	91.49 242	92.26 243	76.87 257	76.16 280	87.91 273	51.67 308	92.34 305	67.74 286	81.16 256	91.52 272

EPNet_dtu		86.49 197	85.94 186	88.14 246	90.24 273	72.82 275	94.11 139	92.20 244	86.66 100	79.42 259	92.36 176	73.52 168	95.81 254	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
thres200		87.21 181	86.24 178	90.12 178	95.36 100	78.53 214	93.26 191	92.10 245	86.42 103	88.00 102	91.11 223	69.24 225	98.00 123	69.58 272	91.04 146	93.83 203

PatchFormer-LS_test		86.02 202	85.13 201	88.70 223	91.52 209	74.12 266	91.19 248	92.09 246	82.71 191	84.30 196	87.24 280	70.87 200	96.98 205	81.04 165	85.17 213	95.00 142

Anonymous20231206		81.03 269	79.77 265	84.82 288	87.85 302	70.26 298	91.42 243	92.08 247	73.67 281	77.75 268	89.25 253	62.43 270	93.08 302	61.50 305	82.00 245	91.12 280

EPNet		91.79 59	91.02 66	94.10 44	90.10 275	85.25 52	96.03 34	92.05 248	92.83 1	87.39 117	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
TDRefinement		79.81 276	77.34 278	87.22 266	79.24 324	75.48 260	93.12 195	92.03 249	76.45 258	75.01 289	91.58 203	49.19 314	96.44 229	70.22 268	69.18 313	89.75 297

DP-MVS		87.25 178	85.36 198	92.90 74	97.65 32	83.24 93	94.81 93	92.00 250	74.99 272	81.92 232	95.00 91	72.66 180	99.05 44	66.92 289	92.33 136	96.40 99

SixPastTwentyTwo		83.91 242	82.90 241	86.92 270	90.99 246	70.67 295	93.48 180	91.99 251	85.54 118	77.62 269	92.11 184	60.59 282	96.87 212	76.05 232	77.75 285	93.20 233

tfpn200view9		87.58 168	86.64 167	90.41 163	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	94.48 176

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

CR-MVSNet		85.35 215	83.76 221	90.12 178	90.58 264	79.34 195	85.24 302	91.96 254	78.27 246	85.55 156	87.87 274	71.03 198	95.61 258	73.96 249	89.36 169	95.40 133

Patchmtry		82.71 252	80.93 255	88.06 247	90.05 277	76.37 253	84.74 304	91.96 254	72.28 294	81.32 239	87.87 274	71.03 198	95.50 265	68.97 278	80.15 273	92.32 260

pmmvs5		84.21 239	82.84 243	88.34 240	88.95 289	76.94 248	92.41 219	91.91 256	75.63 267	80.28 249	91.18 219	64.59 262	95.57 260	77.09 224	83.47 229	92.53 252

test_0402		81.30 267	79.17 271	87.67 253	93.19 174	78.17 228	92.98 203	91.71 257	75.25 269	76.02 284	90.31 239	59.23 289	96.37 232	50.22 320	83.63 227	88.47 312

tpmvs		83.35 249	82.07 246	87.20 267	91.07 244	71.00 293	88.31 281	91.70 258	78.91 235	80.49 248	87.18 281	69.30 224	97.08 198	68.12 285	83.56 228	93.51 226

Patchmatch-test1		85.81 208	84.71 210	89.12 215	92.15 193	76.60 250	91.12 249	91.69 259	83.53 162	85.50 161	88.56 263	66.79 246	95.00 283	72.69 255	90.35 155	95.76 123

pmmvs-eth3d		80.97 270	78.72 275	87.74 251	84.99 310	79.97 170	90.11 257	91.65 260	75.36 268	73.51 296	86.03 294	59.45 288	93.96 291	75.17 238	72.21 298	89.29 300

thres600view7		87.65 158	86.67 162	90.59 149	96.08 79	78.72 210	94.88 89	91.58 261	87.06 91	88.08 101	92.30 178	68.91 228	98.10 112	70.05 271	91.10 142	94.96 146

tpmp4_e23		83.87 244	82.33 245	88.48 235	91.46 211	72.82 275	89.82 261	91.57 262	73.02 288	81.86 233	89.05 254	66.20 253	96.97 206	71.57 259	86.39 203	95.66 126

MDTV_nov1_ep13			83.56 229		91.69 207	69.93 300	87.75 286	91.54 263	78.60 242	84.86 180	88.90 256	69.54 219	96.03 243	70.25 266	88.93 176

tpm cat1		81.96 257	80.27 259	87.01 268	91.09 243	71.02 292	87.38 289	91.53 264	66.25 314	80.17 250	86.35 292	68.22 241	96.15 240	69.16 277	82.29 239	93.86 201

CVMVSNet		84.69 235	84.79 209	84.37 291	91.84 200	64.92 314	93.70 172	91.47 265	66.19 315	86.16 140	95.28 83	67.18 245	93.33 298	80.89 170	90.42 154	94.88 155

tpmrst		85.35 215	84.99 202	86.43 276	90.88 255	67.88 306	88.71 276	91.43 266	80.13 225	86.08 141	88.80 258	73.05 175	96.02 244	82.48 145	83.40 232	95.40 133

EU-MVSNet		81.32 266	80.95 254	82.42 299	88.50 293	63.67 315	93.32 184	91.33 267	64.02 319	80.57 247	92.83 162	61.21 279	92.27 306	76.34 228	80.38 272	91.32 276

PatchmatchNet		85.85 206	84.70 211	89.29 212	91.76 203	75.54 259	88.49 279	91.30 268	81.63 211	85.05 177	88.70 260	71.71 188	96.24 237	74.61 244	89.05 175	96.08 109
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
IB-MVS	80.51 15	85.24 218	83.26 237	91.19 132	92.13 195	79.86 172	91.75 235	91.29 269	83.28 170	80.66 245	88.49 264	61.28 276	98.46 90	80.99 168	79.46 281	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
view600		87.62 162	86.65 163	90.53 152	96.19 71	78.52 215	95.29 61	91.09 270	87.08 87	87.84 105	93.03 154	68.86 229	98.11 108	69.44 273	91.02 147	94.96 146

view800		87.62 162	86.65 163	90.53 152	96.19 71	78.52 215	95.29 61	91.09 270	87.08 87	87.84 105	93.03 154	68.86 229	98.11 108	69.44 273	91.02 147	94.96 146

conf0.05thres1000		87.62 162	86.65 163	90.53 152	96.19 71	78.52 215	95.29 61	91.09 270	87.08 87	87.84 105	93.03 154	68.86 229	98.11 108	69.44 273	91.02 147	94.96 146

tfpn		87.62 162	86.65 163	90.53 152	96.19 71	78.52 215	95.29 61	91.09 270	87.08 87	87.84 105	93.03 154	68.86 229	98.11 108	69.44 273	91.02 147	94.96 146

new-patchmatchnet		76.41 286	75.17 286	80.13 301	82.65 318	59.61 320	87.66 287	91.08 274	78.23 248	69.85 308	83.22 305	54.76 302	91.63 312	64.14 299	64.89 319	89.16 302

test20.03		79.95 275	79.08 272	82.55 298	85.79 306	67.74 307	91.09 250	91.08 274	81.23 218	74.48 293	89.96 245	61.63 274	90.15 314	60.08 308	76.38 289	89.76 296

LF4IMVS		80.37 273	79.07 273	84.27 293	86.64 304	69.87 301	89.39 268	91.05 276	76.38 259	74.97 290	90.00 243	47.85 316	94.25 289	74.55 245	80.82 266	88.69 307

CostFormer		85.77 209	84.94 205	88.26 242	91.16 242	72.58 282	89.47 267	91.04 277	76.26 262	86.45 133	89.97 244	70.74 203	96.86 213	82.35 148	87.07 201	95.34 136

LCM-MVSNet-Re		88.30 136	88.32 118	88.27 241	94.71 123	72.41 283	93.15 194	90.98 278	87.77 76	79.25 260	91.96 191	78.35 103	95.75 256	83.04 136	95.62 82	96.65 95

DWT-MVSNet_test		84.95 225	83.68 226	88.77 220	91.43 215	73.75 269	91.74 236	90.98 278	80.66 222	83.84 202	87.36 278	62.44 269	97.11 196	78.84 206	85.81 206	95.46 131

LCM-MVSNet		66.00 299	62.16 303	77.51 308	64.51 336	58.29 321	83.87 311	90.90 280	48.17 328	54.69 324	73.31 323	16.83 341	86.75 323	65.47 293	61.67 324	87.48 315

AllTest		83.42 246	81.39 250	89.52 205	95.01 110	77.79 238	93.12 195	90.89 281	77.41 252	76.12 281	93.34 138	54.08 305	97.51 147	68.31 282	84.27 220	93.26 231

TestCases				89.52 205	95.01 110	77.79 238		90.89 281	77.41 252	76.12 281	93.34 138	54.08 305	97.51 147	68.31 282	84.27 220	93.26 231

Vis-MVSNet (Re-imp)		89.59 101	89.44 90	90.03 187	95.74 90	75.85 257	95.61 52	90.80 283	87.66 81	87.83 109	95.40 82	76.79 115	96.46 228	78.37 208	96.73 67	97.80 60

RPMNet		83.18 250	80.87 256	90.12 178	90.58 264	79.34 195	85.24 302	90.78 284	71.44 298	85.55 156	82.97 307	70.87 200	95.61 258	61.01 306	89.36 169	95.40 133

OpenMVS_ROB	74.94 19	79.51 278	77.03 282	86.93 269	87.00 303	76.23 255	92.33 222	90.74 285	68.93 308	74.52 292	88.23 268	49.58 312	96.62 218	57.64 312	84.29 219	87.94 314

testgi		80.94 271	80.20 261	83.18 295	87.96 300	66.29 310	91.28 245	90.70 286	83.70 155	78.12 264	92.84 161	51.37 309	90.82 313	63.34 300	82.46 238	92.43 255

MDA-MVSNet-bldmvs		78.85 282	76.31 283	86.46 275	89.76 282	73.88 268	88.79 275	90.42 287	79.16 234	59.18 322	88.33 267	60.20 284	94.04 290	62.00 304	68.96 314	91.48 274

tpm2		84.08 240	82.94 240	87.48 259	91.39 218	71.27 288	89.23 271	90.37 288	71.95 296	84.64 182	89.33 252	67.30 242	96.55 223	75.17 238	87.09 200	94.63 162

TinyColmap		79.76 277	77.69 277	85.97 279	91.71 205	73.12 272	89.55 263	90.36 289	75.03 271	72.03 304	90.19 240	46.22 319	96.19 239	63.11 301	81.03 260	88.59 308

Gipuma		57.99 306	54.91 307	67.24 319	88.51 292	65.59 312	52.21 335	90.33 290	43.58 331	42.84 330	51.18 333	20.29 338	85.07 328	34.77 333	70.45 310	51.05 332
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testus		74.41 290	73.35 288	77.59 307	82.49 319	57.08 323	86.02 295	90.21 291	72.28 294	72.89 301	84.32 300	37.08 326	86.96 322	52.24 316	82.65 236	88.73 305

PatchT		82.68 253	81.27 251	86.89 272	90.09 276	70.94 294	84.06 309	90.15 292	74.91 273	85.63 155	83.57 303	69.37 220	94.87 285	65.19 294	88.50 182	94.84 156

MIMVSNet		82.59 254	80.53 257	88.76 221	91.51 210	78.32 224	86.57 293	90.13 293	79.32 232	80.70 244	88.69 261	52.98 307	93.07 303	66.03 292	88.86 177	94.90 154

dp		81.47 264	80.23 260	85.17 286	89.92 280	65.49 313	86.74 291	90.10 294	76.30 261	81.10 240	87.12 282	62.81 267	95.92 248	68.13 284	79.88 278	94.09 189

MDA-MVSNet_test_wron		79.21 281	77.19 281	85.29 284	88.22 296	72.77 277	85.87 297	90.06 295	74.34 278	62.62 321	87.56 277	66.14 254	91.99 308	66.90 290	73.01 295	91.10 281

PMMVS		85.71 211	84.96 204	87.95 249	88.90 290	77.09 246	88.68 277	90.06 295	72.32 293	86.47 130	90.76 229	72.15 187	94.40 287	81.78 159	93.49 117	92.36 258

YYNet1		79.22 280	77.20 280	85.28 285	88.20 297	72.66 279	85.87 297	90.05 297	74.33 279	62.70 320	87.61 276	66.09 255	92.03 307	66.94 287	72.97 296	91.15 279

tpm		84.73 233	84.02 217	86.87 273	90.33 270	68.90 303	89.06 273	89.94 298	80.85 221	85.75 147	89.86 246	68.54 236	95.97 246	77.76 215	84.05 222	95.75 124

LFMVS		90.08 89	89.13 98	92.95 72	96.71 58	82.32 119	96.08 32	89.91 299	86.79 97	92.15 56	96.81 31	62.60 268	98.34 96	87.18 92	93.90 110	98.19 34

test-LLR		85.87 205	85.41 196	87.25 263	90.95 248	71.67 286	89.55 263	89.88 300	83.41 165	84.54 185	87.95 271	67.25 243	95.11 280	81.82 157	93.37 122	94.97 143

test-mter		84.54 237	83.64 228	87.25 263	90.95 248	71.67 286	89.55 263	89.88 300	79.17 233	84.54 185	87.95 271	55.56 299	95.11 280	81.82 157	93.37 122	94.97 143

test1235678		72.22 293	70.31 294	77.93 306	78.04 325	58.04 322	85.76 299	89.80 302	70.15 306	63.43 319	80.20 316	42.24 323	87.24 321	48.68 322	74.50 293	88.50 309

PVSNet_0	73.20 20	77.22 284	74.83 287	84.37 291	90.70 261	71.10 291	83.09 315	89.67 303	72.81 291	73.93 295	83.13 306	60.79 281	93.70 293	68.54 279	50.84 328	88.30 313

JIA-IIPM		81.04 268	78.98 274	87.25 263	88.64 291	73.48 271	81.75 318	89.61 304	73.19 285	82.05 229	73.71 322	66.07 256	95.87 251	71.18 263	84.60 217	92.41 256

Anonymous20231211		72.97 292	69.63 297	83.00 297	83.05 316	66.91 309	92.69 210	89.45 305	61.06 322	67.50 313	83.46 304	34.34 329	93.61 295	51.11 317	63.97 321	88.48 311

ADS-MVSNet		81.56 262	79.78 264	86.90 271	91.35 225	71.82 285	83.33 313	89.16 306	72.90 289	82.24 225	85.77 295	64.98 260	93.76 292	64.57 297	83.74 224	95.12 138

1111		70.54 297	69.71 296	73.04 311	79.30 322	44.83 335	84.23 307	88.96 307	67.33 311	65.42 316	82.28 309	41.11 324	88.11 319	47.12 324	71.60 303	86.19 316

.test1245		57.63 307	61.79 304	45.14 325	79.30 322	44.83 335	84.23 307	88.96 307	67.33 311	65.42 316	82.28 309	41.11 324	88.11 319	47.12 324	0.39 339	2.46 338

testmv		65.49 300	62.66 301	73.96 310	68.78 331	53.14 330	84.70 305	88.56 309	65.94 316	52.35 325	74.65 321	25.02 334	85.14 327	43.54 328	60.40 326	83.60 317

ADS-MVSNet2		81.66 260	79.71 266	87.50 257	91.35 225	74.19 264	83.33 313	88.48 310	72.90 289	82.24 225	85.77 295	64.98 260	93.20 300	64.57 297	83.74 224	95.12 138

LP		75.51 288	72.15 292	85.61 282	87.86 301	73.93 267	80.20 321	88.43 311	67.39 310	70.05 307	80.56 315	58.18 293	93.18 301	46.28 326	70.36 311	89.71 298

test2356		74.50 289	73.27 289	78.20 303	80.81 320	59.84 318	83.76 312	88.33 312	71.43 299	72.37 303	81.84 311	45.60 320	86.26 324	50.97 318	84.32 218	88.50 309

TESTMET0.1,1		83.74 245	82.85 242	86.42 277	89.96 279	71.21 290	89.55 263	87.88 313	77.41 252	83.37 214	87.31 279	56.71 296	93.65 294	80.62 174	92.85 133	94.40 178

test0.0.03 1		82.41 255	81.69 248	84.59 289	88.23 295	72.89 274	90.24 254	87.83 314	83.41 165	79.86 255	89.78 247	67.25 243	88.99 316	65.18 295	83.42 231	91.90 266

K. test v3		81.59 261	80.15 262	85.91 280	89.89 281	69.42 302	92.57 215	87.71 315	85.56 117	73.44 297	89.71 248	55.58 298	95.52 262	77.17 222	69.76 312	92.78 247

Patchmatch-test		81.37 265	79.30 269	87.58 255	90.92 252	74.16 265	80.99 319	87.68 316	70.52 304	76.63 273	88.81 257	71.21 195	92.76 304	60.01 310	86.93 202	95.83 120

test12356		64.99 301	63.78 300	68.61 318	72.69 328	39.14 338	78.46 323	87.61 317	64.91 317	55.77 323	77.48 319	28.10 331	85.59 326	44.69 327	64.35 320	81.12 322

Patchmatch-RL test		81.67 259	79.96 263	86.81 274	85.42 307	71.23 289	82.17 317	87.50 318	78.47 243	77.19 272	82.50 308	70.81 202	93.48 296	82.66 142	72.89 297	95.71 125

no-one		61.56 303	56.58 305	76.49 309	67.80 334	62.76 317	78.13 324	86.11 319	63.16 320	43.24 329	64.70 328	26.12 333	88.95 317	50.84 319	29.15 331	77.77 324

ANet_high		58.88 305	54.22 308	72.86 312	56.50 340	56.67 325	80.75 320	86.00 320	73.09 287	37.39 331	64.63 329	22.17 336	79.49 334	43.51 329	23.96 335	82.43 321

door-mid								85.49 321

door								85.33 322

PM-MVS		78.11 283	76.12 285	84.09 294	83.54 314	70.08 299	88.97 274	85.27 323	79.93 227	74.73 291	86.43 286	34.70 328	93.48 296	79.43 200	72.06 299	88.72 306

FPMVS		64.63 302	62.55 302	70.88 313	70.80 329	56.71 324	84.42 306	84.42 324	51.78 327	49.57 326	81.61 312	23.49 335	81.48 331	40.61 331	76.25 290	74.46 326

pmmvs3		71.81 295	68.71 298	81.11 300	75.86 326	70.42 297	86.74 291	83.66 325	58.95 324	68.64 312	80.89 314	36.93 327	89.52 315	63.10 302	63.59 322	83.39 318

testpf		71.41 296	72.11 293	69.30 316	84.53 311	59.79 319	62.74 332	83.14 326	71.11 301	68.83 311	81.57 313	46.70 318	84.83 329	74.51 246	75.86 291	63.30 327

MVS-HIRNet		73.70 291	72.20 291	78.18 305	91.81 202	56.42 326	82.94 316	82.58 327	55.24 325	68.88 309	66.48 326	55.32 301	95.13 279	58.12 311	88.42 185	83.01 319

new_pmnet		72.15 294	70.13 295	78.20 303	82.95 317	65.68 311	83.91 310	82.40 328	62.94 321	64.47 318	79.82 317	42.85 322	86.26 324	57.41 313	74.44 294	82.65 320

EPMVS		83.90 243	82.70 244	87.51 256	90.23 274	72.67 278	88.62 278	81.96 329	81.37 217	85.01 178	88.34 266	66.31 251	94.45 286	75.30 237	87.12 199	95.43 132

wuykxyi23d		50.55 309	44.13 311	69.81 315	56.77 338	54.58 329	73.22 329	80.78 330	39.79 333	22.08 338	46.69 335	4.03 345	79.71 333	47.65 323	26.13 333	75.14 325

lessismore_v0				86.04 278	88.46 294	68.78 304		80.59 331		73.01 300	90.11 242	55.39 300	96.43 230	75.06 240	65.06 318	92.90 241

DSMNet-mixed		76.94 285	76.29 284	78.89 302	83.10 315	56.11 327	87.78 285	79.77 332	60.65 323	75.64 287	88.71 259	61.56 275	88.34 318	60.07 309	89.29 171	92.21 263

gg-mvs-nofinetune		81.77 258	79.37 268	88.99 218	90.85 256	77.73 241	86.29 294	79.63 333	74.88 275	83.19 216	69.05 325	60.34 283	96.11 241	75.46 235	94.64 98	93.11 237

PMVS	47.18 22	52.22 308	48.46 309	63.48 320	45.72 341	46.20 334	73.41 328	78.31 334	41.03 332	30.06 334	65.68 327	6.05 343	83.43 330	30.04 334	65.86 317	60.80 329
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
GG-mvs-BLEND				87.94 250	89.73 283	77.91 233	87.80 284	78.23 335		80.58 246	83.86 301	59.88 287	95.33 277	71.20 261	92.22 137	90.60 294

PMMVS2		59.60 304	56.40 306	69.21 317	68.83 330	46.58 333	73.02 330	77.48 336	55.07 326	49.21 327	72.95 324	17.43 340	80.04 332	49.32 321	44.33 329	80.99 323

PNet_i23d		50.48 310	47.18 310	60.36 321	68.59 332	44.56 337	72.75 331	72.61 337	43.92 330	33.91 333	60.19 331	6.16 342	73.52 335	38.50 332	28.04 332	63.01 328

E-PMN		43.23 312	42.29 312	46.03 324	65.58 335	37.41 339	73.51 327	64.62 338	33.99 334	28.47 336	47.87 334	19.90 339	67.91 336	22.23 336	24.45 334	32.77 333

EMVS		42.07 313	41.12 313	44.92 326	63.45 337	35.56 341	73.65 326	63.48 339	33.05 335	26.88 337	45.45 336	21.27 337	67.14 337	19.80 337	23.02 336	32.06 334

MTMP								60.64 340

MVE	39.65 23	43.39 311	38.59 316	57.77 322	56.52 339	48.77 332	55.38 334	58.64 341	29.33 336	28.96 335	52.65 332	4.68 344	64.62 338	28.11 335	33.07 330	59.93 330
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				56.31 323	74.23 327	51.81 331		56.67 342	44.85 329	48.54 328	75.16 320	27.87 332	58.74 339	40.92 330	52.22 327	58.39 331

tmp_tt		35.64 315	39.24 314	24.84 328	14.87 342	23.90 343	62.71 333	51.51 343	6.58 338	36.66 332	62.08 330	44.37 321	30.34 341	52.40 315	22.00 337	20.27 335

N_pmnet		68.89 298	68.44 299	70.23 314	89.07 288	28.79 342	88.06 282	19.50 344	69.47 307	71.86 305	84.93 298	61.24 278	91.75 310	54.70 314	77.15 288	90.15 295

wuyk23d		21.27 317	20.48 318	23.63 329	68.59 332	36.41 340	49.57 336	6.85 345	9.37 337	7.89 339	4.46 342	4.03 345	31.37 340	17.47 338	16.07 338	3.12 336

testmvs		8.92 318	11.52 319	1.12 331	1.06 343	0.46 345	86.02 295	0.65 346	0.62 339	2.74 340	9.52 340	0.31 348	0.45 343	2.38 339	0.39 339	2.46 338

test123		8.76 319	11.22 320	1.39 330	0.85 344	0.97 344	85.76 299	0.35 347	0.54 340	2.45 341	8.14 341	0.60 347	0.48 342	2.16 340	0.17 341	2.71 337

pcd_1.5k_mvsjas		6.64 321	8.86 322	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	79.70 88	0.00 344	0.00 341	0.00 342	0.00 340

sosnet-low-res		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

sosnet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

uncertanet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

Regformer		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

n2								0.00 348

nn								0.00 348

ab-mvs-re		7.82 320	10.43 321	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	93.88 129	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

uanet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

sam_mvs1												71.70 189

sam_mvs												70.60 204

test_post1							88.00 283				9.81 339	69.31 223	95.53 261	76.65 226

test_post											10.29 338	70.57 208	95.91 250

patchmatchnet-post											83.76 302	71.53 192	96.48 226

gm-plane-assit					89.60 284	68.00 305			77.28 255		88.99 255		97.57 143	79.44 199

test9_res														91.91 40	98.71 17	98.07 43

agg_prior2														90.54 59	98.68 22	98.27 29

test_prior4						85.96 41	94.11 139

test_prior2							94.12 137		87.67 79	92.63 43	96.39 50	86.62 23		91.50 47	98.67 24

旧先验2							93.36 183		71.25 300	94.37 11			97.13 195	86.74 98

新几何2							93.11 197

原ACMM2							92.94 205

testdata2													98.75 76	78.30 210

segment_acmp												87.16 19

testdata1							92.15 228		87.94 70

plane_prior7					94.70 124	82.74 109

plane_prior6					94.52 130	82.75 107						74.23 156

plane_prior4											94.86 95

plane_prior3						82.75 107			90.26 25	86.91 124

plane_prior2							95.85 40		90.81 18

plane_prior1					94.59 128

plane_prior						82.73 110	95.21 71		89.66 35						89.88 161

HQP5-MVS						81.56 127

HQP-NCC					94.17 141		94.39 121		88.81 50	85.43 167

ACMP_Plane					94.17 141		94.39 121		88.81 50	85.43 167

BP-MVS														87.11 95

HQP4-MVS										85.43 167			97.96 125			94.51 172

HQP2-MVS												73.83 165

NP-MVS					94.37 136	82.42 117					93.98 122

MDTV_nov1_ep13_2view						55.91 328	87.62 288		73.32 284	84.59 184		70.33 211		74.65 243		95.50 129

ACMMP++_ref															87.47 194

ACMMP++															88.01 191

Test By Simon												80.02 83

High-resolution multi-view benchmark