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.

LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 29	95.54 3	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 8	85.07 36	99.27 3	99.54 1

PMVS	80.48 6	90.08 33	90.66 36	88.34 64	96.71 2	92.97 2	90.31 36	89.57 160	88.51 15	90.11 80	95.12 46	90.98 8	88.92 218	77.55 123	97.07 65	83.13 270
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MPTG		91.27 19	91.26 27	91.29 25	96.59 3	86.29 14	88.94 62	91.81 89	84.07 30	92.00 56	94.40 69	86.63 39	95.28 35	88.59 4	98.31 26	92.30 150

MTAPA		91.52 13	91.60 16	91.29 25	96.59 3	86.29 14	92.02 24	91.81 89	84.07 30	92.00 56	94.40 69	86.63 39	95.28 35	88.59 4	98.31 26	92.30 150

PEN-MVS		90.03 35	91.88 12	84.48 130	96.57 5	58.88 247	88.95 61	93.19 46	91.62 4	96.01 6	96.16 22	87.02 36	95.60 20	78.69 113	98.72 10	98.97 3

PS-CasMVS		90.06 34	91.92 9	84.47 131	96.56 6	58.83 248	89.04 60	92.74 65	91.40 5	96.12 4	96.06 24	87.23 35	95.57 21	79.42 109	98.74 7	99.00 2

DTE-MVSNet		89.98 37	91.91 11	84.21 139	96.51 7	57.84 251	88.93 63	92.84 62	91.92 2	96.16 3	96.23 20	86.95 37	95.99 4	79.05 110	98.57 16	98.80 6

CP-MVSNet		89.27 52	90.91 34	84.37 134	96.34 8	58.61 250	88.66 68	92.06 81	90.78 6	95.67 9	95.17 44	81.80 87	95.54 24	79.00 111	98.69 11	98.95 4

WR-MVS_H		89.91 42	91.31 25	85.71 108	96.32 9	62.39 210	89.54 53	93.31 40	90.21 10	95.57 11	95.66 31	81.42 91	95.90 9	80.94 85	98.80 4	98.84 5

MP-MVS		91.14 23	90.91 34	91.83 18	96.18 10	86.88 11	92.20 22	93.03 54	82.59 44	88.52 122	94.37 72	86.74 38	95.41 30	86.32 26	98.21 32	93.19 124

mPP-MVS		91.69 10	91.47 20	92.37 5	96.04 11	88.48 8	92.72 14	92.60 70	83.09 38	91.54 64	94.25 75	87.67 33	95.51 27	87.21 23	98.11 35	93.12 125

MP-MVS-pluss		90.81 24	91.08 29	89.99 46	95.97 12	79.88 63	88.13 73	94.51 11	75.79 128	92.94 38	94.96 49	88.36 20	95.01 44	90.70 2	98.40 21	95.09 75
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TDRefinement		93.52 2	93.39 3	93.88 1	95.94 13	90.26 4	95.70 2	96.46 2	90.58 8	92.86 41	96.29 18	88.16 26	94.17 66	86.07 32	98.48 19	97.22 26

ACMMP_Plus		90.65 26	91.07 30	89.42 52	95.93 14	79.54 68	89.95 42	93.68 30	77.65 101	91.97 58	94.89 51	88.38 19	95.45 28	89.27 3	97.87 45	93.27 120

HPM-MVS_fast		92.50 5	92.54 5	92.37 5	95.93 14	85.81 27	92.99 11	94.23 16	85.21 24	92.51 48	95.13 45	90.65 11	95.34 32	88.06 10	98.15 34	95.95 51

HSP-MVS		88.63 59	87.84 67	91.02 29	95.76 16	86.14 19	92.75 13	91.01 121	78.43 92	89.16 111	92.25 123	72.03 202	96.36 2	88.21 9	90.93 213	90.55 189

region2R		91.44 17	91.30 26	91.87 16	95.75 17	85.90 23	92.63 17	93.30 41	81.91 53	90.88 75	94.21 76	87.75 30	95.87 10	87.60 16	97.71 50	93.83 103

ACMMPR		91.49 14	91.35 24	91.92 14	95.74 18	85.88 24	92.58 18	93.25 45	81.99 51	91.40 67	94.17 78	87.51 34	95.87 10	87.74 11	97.76 47	93.99 99

TSAR-MVS + MP.		88.14 64	87.82 68	89.09 57	95.72 19	76.74 91	92.49 20	91.19 117	67.85 209	86.63 149	94.84 53	79.58 106	95.96 7	87.62 14	94.50 149	94.56 81

PGM-MVS		91.20 21	90.95 33	91.93 13	95.67 20	85.85 25	90.00 39	93.90 26	80.32 67	91.74 62	94.41 68	88.17 25	95.98 5	86.37 25	97.99 41	93.96 101

XVS		91.54 12	91.36 22	92.08 8	95.64 21	86.25 16	92.64 15	93.33 38	85.07 25	89.99 84	94.03 81	86.57 41	95.80 13	87.35 19	97.62 52	94.20 92

X-MVStestdata		85.04 112	82.70 158	92.08 8	95.64 21	86.25 16	92.64 15	93.33 38	85.07 25	89.99 84	16.05 336	86.57 41	95.80 13	87.35 19	97.62 52	94.20 92

HPM-MVS		92.13 6	92.20 7	91.91 15	95.58 23	84.67 38	93.51 6	94.85 9	82.88 41	91.77 61	93.94 89	90.55 13	95.73 17	88.50 8	98.23 31	95.33 69

ACMMP		91.91 9	91.87 14	92.03 11	95.53 24	85.91 22	93.35 9	94.16 20	82.52 45	92.39 52	94.14 79	89.15 17	95.62 19	87.35 19	98.24 30	94.56 81
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
HFP-MVS		91.30 18	91.39 21	91.02 29	95.43 25	84.66 39	92.58 18	93.29 43	81.99 51	91.47 65	93.96 85	88.35 21	95.56 22	87.74 11	97.74 48	92.85 128

#test#		90.49 30	90.31 41	91.02 29	95.43 25	84.66 39	90.65 34	93.29 43	77.00 116	91.47 65	93.96 85	88.35 21	95.56 22	84.88 39	97.74 48	92.85 128

CP-MVS		91.67 11	91.58 17	91.96 12	95.29 27	87.62 9	93.38 7	93.36 36	83.16 37	91.06 70	94.00 82	88.26 23	95.71 18	87.28 22	98.39 23	92.55 142

VDDNet		84.35 132	85.39 109	81.25 197	95.13 28	59.32 244	85.42 117	81.11 241	86.41 22	87.41 137	96.21 21	73.61 172	90.61 188	66.33 208	96.85 70	93.81 108

CPTT-MVS		89.39 50	88.98 55	90.63 36	95.09 29	86.95 10	92.09 23	92.30 76	79.74 72	87.50 136	92.38 117	81.42 91	93.28 115	83.07 62	97.24 61	91.67 165

ACMM	79.39 9	90.65 26	90.99 31	89.63 49	95.03 30	83.53 44	89.62 50	93.35 37	79.20 80	93.83 28	93.60 94	90.81 9	92.96 128	85.02 38	98.45 20	92.41 145
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		91.49 14	91.53 18	91.39 22	94.98 31	82.95 50	93.52 5	92.79 63	88.22 16	88.53 121	97.64 3	83.45 63	94.55 57	86.02 33	98.60 14	96.67 36

HPM-MVS++		88.93 56	88.45 62	90.38 40	94.92 32	85.85 25	89.70 46	91.27 114	78.20 95	86.69 148	92.28 122	80.36 100	95.06 43	86.17 31	96.49 83	90.22 195

XVG-ACMP-BASELINE		89.98 37	89.84 44	90.41 39	94.91 33	84.50 41	89.49 55	93.98 24	79.68 73	92.09 54	93.89 90	83.80 59	93.10 123	82.67 69	98.04 36	93.64 112

OPM-MVS		89.80 43	89.97 42	89.27 54	94.76 34	79.86 64	86.76 97	92.78 64	78.78 87	92.51 48	93.64 93	88.13 27	93.84 80	84.83 41	97.55 55	94.10 97

Anonymous20231211		90.14 31	91.88 12	84.92 117	94.75 35	64.47 177	90.13 38	92.97 56	91.68 3	95.35 12	98.79 2	93.19 3	91.76 157	71.67 170	98.40 21	98.52 7

LPG-MVS_test		91.47 16	91.68 15	90.82 34	94.75 35	81.69 51	90.00 39	94.27 13	82.35 46	93.67 30	94.82 54	91.18 6	95.52 25	85.36 34	98.73 8	95.23 72

LGP-MVS_train				90.82 34	94.75 35	81.69 51		94.27 13	82.35 46	93.67 30	94.82 54	91.18 6	95.52 25	85.36 34	98.73 8	95.23 72

abl_6		93.02 4	93.16 4	92.60 4	94.73 38	88.99 7	93.26 10	94.19 19	89.11 11	94.43 19	95.27 41	91.86 4	95.09 41	87.54 18	98.02 39	93.71 110

XVG-OURS-SEG-HR		89.59 47	89.37 50	90.28 42	94.47 39	85.95 21	86.84 93	93.91 25	80.07 70	86.75 147	93.26 96	93.64 2	90.93 176	84.60 44	90.75 218	93.97 100

ACMP	79.16 10	90.54 29	90.60 37	90.35 41	94.36 40	80.98 57	89.16 59	94.05 23	79.03 84	92.87 40	93.74 92	90.60 12	95.21 39	82.87 65	98.76 5	94.87 77
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS		89.18 53	88.83 57	90.23 43	94.28 41	86.11 20	85.91 109	93.60 33	80.16 69	89.13 112	93.44 95	83.82 58	90.98 174	83.86 54	95.30 124	93.60 114

MIMVSNet1		83.63 152	84.59 129	80.74 202	94.06 42	62.77 201	82.72 179	84.53 225	77.57 103	90.34 78	95.92 26	76.88 136	85.83 251	61.88 232	97.42 57	93.62 113

TranMVSNet+NR-MVSNet		87.86 67	88.76 60	85.18 114	94.02 43	64.13 179	84.38 131	91.29 113	84.88 27	92.06 55	93.84 91	86.45 43	93.73 81	73.22 156	98.66 12	97.69 12

新几何1				82.95 170	93.96 44	78.56 75		80.24 245	55.45 279	83.93 192	91.08 154	71.19 207	88.33 225	65.84 213	93.07 182	81.95 285

1121		80.86 185	79.81 195	84.02 142	93.93 45	78.70 74	81.64 208	80.18 246	55.43 280	83.67 194	91.15 149	71.29 206	91.41 166	67.95 200	93.06 183	81.96 284

SteuartSystems-ACMMP		91.16 22	91.36 22	90.55 37	93.91 46	80.97 58	91.49 29	93.48 35	82.82 42	92.60 47	93.97 83	88.19 24	96.29 3	87.61 15	98.20 33	94.39 90
Skip Steuart: Steuart Systems R&D Blog.
LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 19	93.73 47	85.72 28	96.79 1	95.51 4	88.86 13	95.63 10	96.99 8	84.81 52	93.16 120	91.10 1	97.53 56	96.58 39
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
COLMAP_ROB	83.01 3	91.97 8	91.95 8	92.04 10	93.68 48	86.15 18	93.37 8	95.10 7	90.28 9	92.11 53	95.03 47	89.75 15	94.93 46	79.95 101	98.27 29	95.04 76
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DeepC-MVS	82.31 4	89.15 54	89.08 52	89.37 53	93.64 49	79.07 70	88.54 69	94.20 17	73.53 148	89.71 95	94.82 54	85.09 50	95.77 15	84.17 50	98.03 38	93.26 121
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
mvs_tets		89.78 44	89.27 51	91.30 24	93.51 50	84.79 36	89.89 44	90.63 126	70.00 191	94.55 18	96.67 11	87.94 29	93.59 91	84.27 48	95.97 103	95.52 65

HQP_MVS		87.75 70	87.43 75	88.70 60	93.45 51	76.42 95	89.45 56	93.61 31	79.44 77	86.55 150	92.95 104	74.84 151	95.22 37	80.78 88	95.83 110	94.46 86

plane_prior7					93.45 51	77.31 85

WR-MVS		83.56 153	84.40 135	81.06 200	93.43 53	54.88 271	78.67 245	85.02 221	81.24 59	90.74 76	91.56 138	72.85 188	91.08 172	68.00 198	98.04 36	97.23 25

jajsoiax		89.41 49	88.81 58	91.19 28	93.38 54	84.72 37	89.70 46	90.29 142	69.27 195	94.39 20	96.38 15	86.02 48	93.52 101	83.96 51	95.92 106	95.34 68

PS-MVSNAJss		88.31 62	87.90 66	89.56 51	93.31 55	77.96 78	87.94 75	91.97 84	70.73 185	94.19 24	96.67 11	76.94 130	94.57 55	83.07 62	96.28 89	96.15 42

test222					93.31 55	76.54 92	79.38 235	77.79 254	52.59 293	82.36 206	90.84 164	66.83 221			91.69 202	81.25 296

DU-MVS		86.80 81	86.99 81	86.21 97	93.24 57	67.02 162	83.16 168	92.21 77	81.73 55	90.92 72	91.97 125	77.20 124	93.99 71	74.16 145	98.35 24	97.61 13

NR-MVSNet		86.00 97	86.22 95	85.34 112	93.24 57	64.56 176	82.21 194	90.46 130	80.99 61	88.42 124	91.97 125	77.56 121	93.85 78	72.46 164	98.65 13	97.61 13

OurMVSNet-221017-0		90.01 36	89.74 46	90.83 33	93.16 59	80.37 59	91.91 27	93.11 48	81.10 60	95.32 13	97.24 6	72.94 187	94.85 48	85.07 36	97.78 46	97.26 23

pcd1.5k->3k		38.83 313	41.11 314	32.01 324	93.13 60	0.00 344	0.00 335	91.38 111	0.00 339	0.00 340	0.00 341	89.24 16	0.00 342	0.00 339	96.24 92	96.02 48

UniMVSNet (Re)		86.87 78	86.98 82	86.55 87	93.11 61	68.48 154	83.80 146	92.87 59	80.37 65	89.61 103	91.81 132	77.72 119	94.18 64	75.00 142	98.53 17	96.99 34

APD-MVS_3200maxsize		92.05 7	92.24 6	91.48 20	93.02 62	85.17 31	92.47 21	95.05 8	87.65 19	93.21 36	94.39 71	90.09 14	95.08 42	86.67 24	97.60 54	94.18 94

ACMH+	77.89 11	90.73 25	91.50 19	88.44 62	93.00 63	76.26 97	89.65 49	95.55 3	87.72 18	93.89 27	94.94 50	91.62 5	93.44 107	78.35 115	98.76 5	95.61 64

APDe-MVS		91.22 20	91.92 9	89.14 56	92.97 64	78.04 77	92.84 12	94.14 21	83.33 35	93.90 26	95.73 29	88.77 18	96.41 1	87.60 16	97.98 42	92.98 127

114514_t		83.10 161	82.54 163	84.77 122	92.90 65	69.10 152	86.65 102	90.62 127	54.66 283	81.46 218	90.81 165	76.98 129	94.38 58	72.62 163	96.18 93	90.82 181

testdata				79.54 217	92.87 66	72.34 120		80.14 247	59.91 264	85.47 168	91.75 134	67.96 217	85.24 255	68.57 196	92.18 200	81.06 301

CNVR-MVS		87.81 69	87.68 71	88.21 65	92.87 66	77.30 86	85.25 118	91.23 115	77.31 111	87.07 143	91.47 140	82.94 68	94.71 51	84.67 43	96.27 91	92.62 141

UniMVSNet_NR-MVSNet		86.84 80	87.06 79	86.17 100	92.86 68	67.02 162	82.55 183	91.56 94	83.08 39	90.92 72	91.82 131	78.25 115	93.99 71	74.16 145	98.35 24	97.49 16

plane_prior1					92.83 69

原ACMM1				84.60 127	92.81 70	74.01 108		91.50 96	62.59 246	82.73 205	90.67 170	76.53 137	94.25 61	69.24 186	95.69 115	85.55 242

plane_prior6					92.61 71	76.54 92						74.84 151

APD-MVS		89.54 48	89.63 48	89.26 55	92.57 72	81.34 56	90.19 37	93.08 50	80.87 62	91.13 69	93.19 97	86.22 46	95.97 6	82.23 73	97.18 63	90.45 191
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_0402		88.65 58	89.58 49	85.88 105	92.55 73	72.22 123	84.01 137	89.44 162	88.63 14	94.38 21	95.77 28	86.38 45	93.59 91	79.84 102	95.21 125	91.82 162

SixPastTwentyTwo		87.20 74	87.45 74	86.45 89	92.52 74	69.19 151	87.84 78	88.05 179	81.66 56	94.64 17	96.53 14	65.94 225	94.75 50	83.02 64	96.83 72	95.41 67

ACMH	76.49 14	89.34 51	91.14 28	83.96 145	92.50 75	70.36 141	89.55 51	93.84 27	81.89 54	94.70 16	95.44 39	90.69 10	88.31 226	83.33 58	98.30 28	93.20 123
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPNet		80.25 192	81.68 172	75.94 255	92.46 76	47.98 311	76.70 260	81.67 239	73.45 149	84.87 173	92.82 107	74.66 156	86.51 243	61.66 235	96.85 70	93.33 119

agg_prior3		85.76 103	84.95 116	88.16 66	92.43 77	79.92 62	83.98 138	90.03 150	65.11 231	83.66 195	90.64 173	81.00 94	93.67 83	81.21 80	96.54 80	90.88 178

F-COLMAP		84.97 115	83.42 150	89.63 49	92.39 78	83.40 45	88.83 64	91.92 86	73.19 157	80.18 235	89.15 196	77.04 128	93.28 115	65.82 214	92.28 196	92.21 155

test_djsdf		89.62 46	89.01 53	91.45 21	92.36 79	82.98 49	91.98 25	90.08 148	71.54 179	94.28 23	96.54 13	81.57 89	94.27 59	86.26 27	96.49 83	97.09 30

TEST9					92.34 80	79.70 66	83.94 139	90.32 135	65.41 229	84.49 183	90.97 158	82.03 81	93.63 86

train_agg		85.98 100	85.28 110	88.07 68	92.34 80	79.70 66	83.94 139	90.32 135	65.79 222	84.49 183	90.97 158	81.93 83	93.63 86	81.21 80	96.54 80	90.88 178

NCCC		87.36 71	86.87 85	88.83 58	92.32 82	78.84 73	86.58 104	91.09 119	78.77 88	84.85 174	90.89 162	80.85 95	95.29 33	81.14 82	95.32 121	92.34 149

FC-MVSNet-test		85.93 101	87.05 80	82.58 176	92.25 83	56.44 261	85.75 112	93.09 49	77.33 110	91.94 59	94.65 59	74.78 153	93.41 109	75.11 140	98.58 15	97.88 10

CDPH-MVS		86.17 96	85.54 106	88.05 71	92.25 83	75.45 100	83.85 143	92.01 82	65.91 221	86.19 157	91.75 134	83.77 60	94.98 45	77.43 126	96.71 75	93.73 109

pmmvs6		86.52 87	88.06 64	81.90 185	92.22 85	62.28 216	84.66 126	89.15 165	83.54 34	89.85 91	97.32 4	88.08 28	86.80 240	70.43 179	97.30 60	96.62 37

EG-PatchMatch MVS		84.08 142	84.11 142	83.98 144	92.22 85	72.61 117	82.20 196	87.02 199	72.63 164	88.86 114	91.02 156	78.52 111	91.11 171	73.41 155	91.09 207	88.21 214

test_8					92.09 87	78.87 72	83.82 144	90.31 137	65.79 222	84.36 186	90.96 160	81.93 83	93.44 107

Vis-MVSNet		86.86 79	86.58 89	87.72 73	92.09 87	77.43 83	87.35 84	92.09 80	78.87 86	84.27 190	94.05 80	78.35 114	93.65 84	80.54 92	91.58 204	92.08 157
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		86.66 84	86.82 88	86.17 100	92.05 89	66.87 164	91.21 33	88.64 170	86.30 23	89.60 104	92.59 112	69.22 212	94.91 47	73.89 149	97.89 44	96.72 35

旧先验1					91.97 90	71.77 129		81.78 238			91.84 129	73.92 165			93.65 168	83.61 260

v7n		90.13 32	90.96 32	87.65 75	91.95 91	71.06 137	89.99 41	93.05 51	86.53 21	94.29 22	96.27 19	82.69 70	94.08 69	86.25 29	97.63 51	97.82 11

NP-MVS					91.95 91	74.55 105					90.17 182

OMC-MVS		88.19 63	87.52 72	90.19 44	91.94 93	81.68 53	87.49 83	93.17 47	76.02 124	88.64 119	91.22 144	84.24 57	93.37 110	77.97 121	97.03 66	95.52 65

FIs		85.35 108	86.27 94	82.60 175	91.86 94	57.31 255	85.10 120	93.05 51	75.83 127	91.02 71	93.97 83	73.57 173	92.91 132	73.97 148	98.02 39	97.58 15

MSLP-MVS++		85.00 114	86.03 99	81.90 185	91.84 95	71.56 135	86.75 98	93.02 55	75.95 125	87.12 140	89.39 193	77.98 116	89.40 210	77.46 124	94.78 141	84.75 250

DP-MVS Recon		84.05 143	83.22 153	86.52 88	91.73 96	75.27 101	83.23 167	92.40 73	72.04 173	82.04 210	88.33 210	77.91 118	93.95 76	66.17 209	95.12 128	90.34 194

SD-MVS		88.96 55	89.88 43	86.22 95	91.63 97	77.07 87	89.82 45	93.77 29	78.90 85	92.88 39	92.29 121	86.11 47	90.22 196	86.24 30	97.24 61	91.36 172

AllTest		87.97 66	87.40 76	89.68 47	91.59 98	83.40 45	89.50 54	95.44 5	79.47 75	88.00 129	93.03 100	82.66 71	91.47 162	70.81 172	96.14 96	94.16 95

TestCases				89.68 47	91.59 98	83.40 45		95.44 5	79.47 75	88.00 129	93.03 100	82.66 71	91.47 162	70.81 172	96.14 96	94.16 95

MCST-MVS		84.36 130	83.93 147	85.63 109	91.59 98	71.58 134	83.52 155	92.13 79	61.82 252	83.96 191	89.75 188	79.93 105	93.46 106	78.33 116	94.34 152	91.87 161

agg_prior1		85.72 104	85.20 111	87.28 81	91.58 101	77.69 79	83.69 150	90.30 139	66.29 217	84.32 187	91.07 155	82.13 78	93.18 118	81.02 83	96.36 86	90.98 173

agg_prior					91.58 101	77.69 79		90.30 139		84.32 187			93.18 118

PVSNet_Blended_VisFu		81.55 176	80.49 187	84.70 125	91.58 101	73.24 113	84.21 132	91.67 92	62.86 244	80.94 223	87.16 226	67.27 219	92.87 133	69.82 182	88.94 235	87.99 218

EPP-MVSNet		85.47 107	85.04 113	86.77 84	91.52 104	69.37 146	91.63 28	87.98 182	81.51 58	87.05 144	91.83 130	66.18 224	95.29 33	70.75 174	96.89 69	95.64 59

DeepPCF-MVS	81.24 5	87.28 73	86.21 96	90.49 38	91.48 105	84.90 34	83.41 160	92.38 75	70.25 190	89.35 109	90.68 169	82.85 69	94.57 55	79.55 105	95.95 104	92.00 158

Baseline_NR-MVSNet		84.00 145	85.90 100	78.29 229	91.47 106	53.44 279	82.29 190	87.00 200	79.06 83	89.55 105	95.72 30	77.20 124	86.14 247	72.30 165	98.51 18	95.28 70

HyFIR lowres test		75.12 233	72.66 246	82.50 179	91.44 107	65.19 172	72.47 287	87.31 189	46.79 319	80.29 233	84.30 262	52.70 278	92.10 148	51.88 289	86.73 258	90.22 195

DP-MVS		88.60 60	89.01 53	87.36 80	91.30 108	77.50 82	87.55 81	92.97 56	87.95 17	89.62 101	92.87 106	84.56 54	93.89 77	77.65 122	96.62 77	90.70 183

DeepC-MVS_fast	80.27 8	86.23 94	85.65 105	87.96 72	91.30 108	76.92 88	87.19 87	91.99 83	70.56 186	84.96 170	90.69 168	80.01 103	95.14 40	78.37 114	95.78 112	91.82 162
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+	83.92 2	89.97 39	89.66 47	90.92 32	91.27 110	81.66 54	91.25 32	94.13 22	88.89 12	88.83 116	94.26 74	77.55 122	95.86 12	84.88 39	95.87 108	95.24 71

HQP-NCC					91.19 111		84.77 122		73.30 153	80.55 230

ACMP_Plane					91.19 111		84.77 122		73.30 153	80.55 230

HQP-MVS		84.61 121	84.06 143	86.27 93	91.19 111	70.66 139	84.77 122	92.68 66	73.30 153	80.55 230	90.17 182	72.10 198	94.61 53	77.30 127	94.47 150	93.56 116

VDD-MVS		84.23 136	84.58 130	83.20 166	91.17 114	65.16 173	83.25 165	84.97 223	79.79 71	87.18 139	94.27 73	74.77 154	90.89 179	69.24 186	96.54 80	93.55 118

K. test v3		85.14 109	84.73 119	86.37 90	91.13 115	69.63 145	85.45 116	76.68 262	84.06 32	92.44 50	96.99 8	62.03 237	94.65 52	80.58 91	93.24 180	94.83 80

lessismore_v0				85.95 102	91.10 116	70.99 138		70.91 300		91.79 60	94.42 67	61.76 238	92.93 130	79.52 108	93.03 184	93.93 102

TransMVSNet (Re)		84.02 144	85.74 102	78.85 221	91.00 117	55.20 270	82.29 190	87.26 190	79.65 74	88.38 126	95.52 37	83.00 67	86.88 238	67.97 199	96.60 78	94.45 88

wuykxyi23d		88.46 61	88.80 59	87.44 79	90.96 118	93.03 1	85.85 111	81.96 235	74.58 138	98.58 2	97.29 5	87.73 31	87.31 233	82.84 67	99.41 1	81.99 283

PAPM_NR		83.23 158	83.19 155	83.33 164	90.90 119	65.98 168	88.19 72	90.78 123	78.13 97	80.87 225	87.92 218	73.49 175	92.42 141	70.07 180	88.40 239	91.60 167

CSCG		86.26 92	86.47 91	85.60 110	90.87 120	74.26 107	87.98 74	91.85 87	80.35 66	89.54 107	88.01 214	79.09 108	92.13 146	75.51 137	95.06 130	90.41 192

PLC	73.85 16	82.09 172	80.31 188	87.45 78	90.86 121	80.29 60	85.88 110	90.65 125	68.17 203	76.32 256	86.33 234	73.12 185	92.61 139	61.40 238	90.02 227	89.44 203
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test12				86.57 86	90.74 122	72.63 116		90.69 124		82.76 204		79.20 107	94.80 49		95.32 121	92.27 152

ITE_SJBPF				90.11 45	90.72 123	84.97 33		90.30 139	81.56 57	90.02 83	91.20 147	82.40 75	90.81 181	73.58 152	94.66 146	94.56 81

TAMVS		78.08 205	76.36 218	83.23 165	90.62 124	72.87 114	79.08 240	80.01 248	61.72 254	81.35 220	86.92 229	63.96 231	88.78 220	50.61 290	93.01 185	88.04 217

test_prior3		86.31 91	86.31 93	86.32 91	90.59 125	71.99 127	83.37 161	92.85 60	75.43 131	84.58 181	91.57 136	81.92 85	94.17 66	79.54 106	96.97 67	92.80 130

test_prior				86.32 91	90.59 125	71.99 127		92.85 60					94.17 66			92.80 130

ambc				82.98 169	90.55 127	64.86 174	88.20 71	89.15 165		89.40 108	93.96 85	71.67 205	91.38 168	78.83 112	96.55 79	92.71 133

Test_1112_low_res		73.90 242	73.08 242	76.35 251	90.35 128	55.95 262	73.40 286	86.17 206	50.70 309	73.14 279	85.94 239	58.31 257	85.90 250	56.51 265	83.22 286	87.20 227

VPA-MVSNet		83.47 156	84.73 119	79.69 213	90.29 129	57.52 254	81.30 215	88.69 169	76.29 120	87.58 134	94.44 66	80.60 98	87.20 234	66.60 207	96.82 73	94.34 91

FMVSNet1		84.55 123	85.45 108	81.85 188	90.27 130	61.05 231	86.83 94	88.27 176	78.57 91	89.66 97	95.64 33	75.43 142	90.68 185	69.09 189	95.33 120	93.82 105

MVS_111021_HR		84.63 120	84.34 140	85.49 111	90.18 131	75.86 99	79.23 239	87.13 195	73.35 151	85.56 166	89.34 194	83.60 62	90.50 190	76.64 132	94.05 156	90.09 200

RPSCF		88.00 65	86.93 84	91.22 27	90.08 132	89.30 6	89.68 48	91.11 118	79.26 79	89.68 96	94.81 57	82.44 74	87.74 230	76.54 133	88.74 238	96.61 38

nrg030		87.85 68	88.49 61	85.91 103	90.07 133	69.73 143	87.86 76	94.20 17	74.04 143	92.70 45	94.66 58	85.88 49	91.50 161	79.72 103	97.32 59	96.50 40

v748		88.91 57	89.82 45	86.19 99	90.06 134	68.53 153	88.81 65	91.48 98	84.36 28	94.19 24	95.98 25	82.52 73	92.67 137	84.30 47	96.67 76	97.37 20

AdaColmap		83.66 151	83.69 149	83.57 158	90.05 135	72.26 122	86.29 108	90.00 151	78.19 96	81.65 216	87.16 226	83.40 64	94.24 62	61.69 234	94.76 144	84.21 255

pm-mvs1		83.69 150	84.95 116	79.91 209	90.04 136	59.66 241	82.43 185	87.44 187	75.52 130	87.85 131	95.26 42	81.25 93	85.65 253	68.74 193	96.04 101	94.42 89

CHOSEN 1792x2688		72.45 250	70.56 259	78.13 231	90.02 137	63.08 195	68.72 298	83.16 228	42.99 328	75.92 260	85.46 245	57.22 264	85.18 257	49.87 294	81.67 295	86.14 236

anonymousdsp		89.73 45	88.88 56	92.27 7	89.82 138	86.67 12	90.51 35	90.20 147	69.87 192	95.06 14	96.14 23	84.28 56	93.07 127	87.68 13	96.34 87	97.09 30

1112_ss		74.82 237	73.74 236	78.04 233	89.57 139	60.04 239	76.49 263	87.09 198	54.31 284	73.66 277	79.80 300	60.25 245	86.76 241	58.37 256	84.15 282	87.32 226

PCF-MVS	74.62 15	82.15 171	80.92 183	85.84 106	89.43 140	72.30 121	80.53 221	91.82 88	57.36 271	87.81 132	89.92 185	77.67 120	93.63 86	58.69 251	95.08 129	91.58 168
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVP-Stereo		75.81 230	73.51 239	82.71 174	89.35 141	73.62 109	80.06 224	85.20 216	60.30 262	73.96 275	87.94 216	57.89 259	89.45 208	52.02 287	74.87 316	85.06 247
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CNLPA		83.55 154	83.10 156	84.90 118	89.34 142	83.87 43	84.54 129	88.77 167	79.09 82	83.54 198	88.66 203	74.87 150	81.73 278	66.84 206	92.29 195	89.11 206

TSAR-MVS + GP.		83.95 146	82.69 159	87.72 73	89.27 143	81.45 55	83.72 149	81.58 240	74.73 137	85.66 163	86.06 238	72.56 196	92.69 136	75.44 138	95.21 125	89.01 210

MVS_111021_LR		84.28 134	83.76 148	85.83 107	89.23 144	83.07 48	80.99 218	83.56 227	72.71 163	86.07 158	89.07 197	81.75 88	86.19 246	77.11 129	93.36 173	88.24 213

LFMVS		80.15 195	80.56 185	78.89 220	89.19 145	55.93 263	85.22 119	73.78 277	82.96 40	84.28 189	92.72 111	57.38 262	90.07 203	63.80 223	95.75 113	90.68 184

CLD-MVS		83.18 159	82.64 160	84.79 121	89.05 146	67.82 159	77.93 250	92.52 71	68.33 202	85.07 169	81.54 292	82.06 79	92.96 128	69.35 185	97.91 43	93.57 115
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LS3D		90.60 28	90.34 40	91.38 23	89.03 147	84.23 42	93.58 4	94.68 10	90.65 7	90.33 79	93.95 88	84.50 55	95.37 31	80.87 86	95.50 117	94.53 85

CDS-MVSNet		77.32 211	75.40 226	83.06 168	89.00 148	72.48 119	77.90 251	82.17 234	60.81 259	78.94 243	83.49 270	59.30 252	88.76 221	54.64 279	92.37 194	87.93 220
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v13		87.31 72	88.10 63	84.94 116	88.84 149	63.75 183	87.85 77	91.47 101	79.12 81	93.72 29	95.82 27	75.20 145	93.58 94	84.76 42	96.16 94	97.48 17

Vis-MVSNet (Re-imp)		77.82 206	77.79 205	77.92 235	88.82 150	51.29 293	83.28 163	71.97 289	74.04 143	82.23 207	89.78 187	57.38 262	89.41 209	57.22 262	95.41 118	93.05 126

TAPA-MVS	77.73 12	85.71 105	84.83 118	88.37 63	88.78 151	79.72 65	87.15 89	93.50 34	69.17 196	85.80 162	89.56 191	80.76 96	92.13 146	73.21 160	95.51 116	93.25 122
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v12		87.15 75	87.91 65	84.84 119	88.69 152	63.52 186	87.58 80	91.46 102	78.74 89	93.57 32	95.66 31	74.94 149	93.57 95	84.50 45	96.08 99	97.43 18

v52		89.97 39	90.60 37	88.07 68	88.69 152	72.01 125	91.35 30	92.64 68	82.22 48	95.97 8	96.31 16	84.82 51	93.98 73	88.59 4	94.83 139	98.23 8

V4		89.97 39	90.60 37	88.07 68	88.69 152	72.01 125	91.35 30	92.64 68	82.22 48	95.98 7	96.31 16	84.80 53	93.98 73	88.59 4	94.83 139	98.23 8

FPMVS		72.29 252	72.00 252	73.14 268	88.63 155	85.00 32	74.65 277	67.39 310	71.94 175	77.80 247	87.66 221	50.48 283	75.83 294	49.95 292	79.51 303	58.58 329

BH-untuned		80.96 184	80.99 181	80.84 201	88.55 156	68.23 155	80.33 223	88.46 171	72.79 162	86.55 150	86.76 230	74.72 155	91.77 156	61.79 233	88.99 234	82.52 276

V9		86.96 76	87.70 70	84.74 123	88.52 157	63.27 192	87.31 85	91.45 104	78.28 94	93.43 33	95.45 38	74.59 157	93.57 95	84.23 49	96.01 102	97.38 19

v11		86.96 76	87.78 69	84.51 128	88.50 158	62.60 206	87.21 86	91.63 93	78.08 98	93.40 34	95.56 36	75.07 146	93.57 95	84.46 46	96.08 99	97.36 21

ab-mvs		79.67 197	80.56 185	76.99 245	88.48 159	56.93 257	84.70 125	86.06 207	68.95 200	80.78 226	93.08 99	75.30 144	84.62 262	56.78 264	90.90 214	89.43 204

PHI-MVS		86.38 89	85.81 101	88.08 67	88.44 160	77.34 84	89.35 58	93.05 51	73.15 158	84.76 175	87.70 220	78.87 110	94.18 64	80.67 90	96.29 88	92.73 132

V14		86.75 82	87.46 73	84.62 126	88.35 161	63.00 197	87.02 91	91.42 107	77.78 100	93.27 35	95.23 43	74.22 159	93.56 98	83.95 52	95.93 105	97.31 22

xiu_mvs_v1_base_debu		80.84 186	80.14 191	82.93 171	88.31 162	71.73 130	79.53 231	87.17 192	65.43 226	79.59 237	82.73 281	76.94 130	90.14 199	73.22 156	88.33 240	86.90 230

xiu_mvs_v1_base		80.84 186	80.14 191	82.93 171	88.31 162	71.73 130	79.53 231	87.17 192	65.43 226	79.59 237	82.73 281	76.94 130	90.14 199	73.22 156	88.33 240	86.90 230

xiu_mvs_v1_base_debi		80.84 186	80.14 191	82.93 171	88.31 162	71.73 130	79.53 231	87.17 192	65.43 226	79.59 237	82.73 281	76.94 130	90.14 199	73.22 156	88.33 240	86.90 230

MG-MVS		80.32 191	80.94 182	78.47 228	88.18 165	52.62 286	82.29 190	85.01 222	72.01 174	79.24 241	92.54 115	69.36 211	93.36 111	70.65 176	89.19 233	89.45 202

PM-MVS		80.20 194	79.00 198	83.78 149	88.17 166	86.66 13	81.31 213	66.81 316	69.64 193	88.33 127	90.19 180	64.58 228	83.63 270	71.99 169	90.03 226	81.06 301

v10		86.54 86	87.10 78	84.84 119	88.16 167	63.28 191	86.64 103	92.20 78	75.42 133	92.81 42	94.50 64	74.05 162	94.06 70	83.88 53	96.28 89	97.17 28

v15		86.56 85	87.25 77	84.51 128	88.15 168	62.72 202	86.72 101	91.40 109	77.38 105	93.11 37	95.00 48	73.93 164	93.55 99	83.67 56	95.86 109	97.26 23

v7		84.81 117	85.00 114	84.23 138	88.15 168	63.27 192	83.79 147	91.39 110	71.10 183	90.07 81	91.28 142	74.04 163	93.63 86	81.48 79	93.67 167	95.79 52

v17		86.32 90	86.95 83	84.44 132	88.00 170	62.62 205	86.74 99	91.48 98	77.17 113	92.74 43	94.56 60	73.74 168	93.53 100	83.27 59	94.87 138	97.18 27

canonicalmvs		85.50 106	86.14 97	83.58 157	87.97 171	67.13 161	87.55 81	94.32 12	73.44 150	88.47 123	87.54 223	86.45 43	91.06 173	75.76 136	93.76 164	92.54 143

v16		86.24 93	86.85 86	84.43 133	87.96 172	62.59 207	86.73 100	91.48 98	77.17 113	92.67 46	94.55 61	73.63 169	93.52 101	83.26 60	94.16 153	97.17 28

VNet		79.31 198	80.27 189	76.44 250	87.92 173	53.95 275	75.58 271	84.35 226	74.39 141	82.23 207	90.72 167	72.84 189	84.39 264	60.38 244	93.98 158	90.97 174

view600		76.79 216	76.54 213	77.56 239	87.91 174	50.77 299	81.92 200	71.35 296	77.38 105	84.62 177	88.40 206	45.18 307	89.26 211	58.58 252	93.49 169	92.66 135

view800		76.79 216	76.54 213	77.56 239	87.91 174	50.77 299	81.92 200	71.35 296	77.38 105	84.62 177	88.40 206	45.18 307	89.26 211	58.58 252	93.49 169	92.66 135

conf0.05thres1000		76.79 216	76.54 213	77.56 239	87.91 174	50.77 299	81.92 200	71.35 296	77.38 105	84.62 177	88.40 206	45.18 307	89.26 211	58.58 252	93.49 169	92.66 135

tfpn		76.79 216	76.54 213	77.56 239	87.91 174	50.77 299	81.92 200	71.35 296	77.38 105	84.62 177	88.40 206	45.18 307	89.26 211	58.58 252	93.49 169	92.66 135

v8		86.22 95	86.83 87	84.36 135	87.82 178	62.35 211	86.42 106	91.33 112	76.78 117	92.73 44	94.48 65	73.41 176	93.72 82	83.10 61	95.41 118	97.01 33

v1neww		84.43 127	84.66 125	83.75 150	87.81 179	62.34 212	83.59 151	90.27 143	72.33 169	89.93 88	91.22 144	73.28 180	93.29 112	80.25 97	93.25 178	95.62 60

v7new		84.43 127	84.66 125	83.75 150	87.81 179	62.34 212	83.59 151	90.27 143	72.33 169	89.93 88	91.22 144	73.28 180	93.29 112	80.25 97	93.25 178	95.62 60

v6		84.43 127	84.66 125	83.75 150	87.81 179	62.34 212	83.59 151	90.26 145	72.33 169	89.94 87	91.19 148	73.30 179	93.29 112	80.26 96	93.26 177	95.62 60

alignmvs		83.94 147	83.98 145	83.80 147	87.80 182	67.88 158	84.54 129	91.42 107	73.27 156	88.41 125	87.96 215	72.33 197	90.83 180	76.02 135	94.11 154	92.69 134

v1192		84.57 122	84.69 123	84.21 139	87.75 183	62.88 199	83.02 170	91.43 105	69.08 198	89.98 86	90.89 162	72.70 192	93.62 90	82.41 70	94.97 133	96.13 43

v18		85.99 99	86.55 90	84.30 137	87.73 184	62.29 215	86.40 107	91.49 97	76.64 118	92.40 51	94.20 77	73.28 180	93.52 101	82.87 65	93.99 157	97.09 30

PatchMatch-RL		74.48 238	73.22 241	78.27 230	87.70 185	85.26 30	75.92 267	70.09 302	64.34 236	76.09 259	81.25 294	65.87 226	78.07 287	53.86 281	83.82 283	71.48 317

v1144		84.54 125	84.72 121	84.00 143	87.67 186	62.55 208	82.97 171	90.93 122	70.32 189	89.80 93	90.99 157	73.50 174	93.48 105	81.69 78	94.65 147	95.97 49

v1240		84.30 133	84.51 132	83.65 155	87.65 187	61.26 228	82.85 174	91.54 95	67.94 207	90.68 77	90.65 171	71.71 204	93.64 85	82.84 67	94.78 141	96.07 45

v1921920		84.23 136	84.37 139	83.79 148	87.64 188	61.71 219	82.91 173	91.20 116	67.94 207	90.06 82	90.34 176	72.04 201	93.59 91	82.32 72	94.91 134	96.07 45

v144192		84.24 135	84.41 134	83.71 154	87.59 189	61.57 224	82.95 172	91.03 120	67.82 210	89.80 93	90.49 174	73.28 180	93.51 104	81.88 77	94.89 135	96.04 47

Fast-Effi-MVS+		81.04 183	80.57 184	82.46 180	87.50 190	63.22 194	78.37 246	89.63 158	68.01 204	81.87 212	82.08 287	82.31 76	92.65 138	67.10 202	88.30 244	91.51 170

pmmvs-eth3d		78.42 203	77.04 209	82.57 178	87.44 191	74.41 106	80.86 220	79.67 249	55.68 278	84.69 176	90.31 179	60.91 241	85.42 254	62.20 230	91.59 203	87.88 221

MVS_0304		84.88 116	83.96 146	87.64 76	87.43 192	74.83 103	84.18 133	93.30 41	77.48 104	77.39 250	88.46 205	74.53 158	95.74 16	78.09 120	94.75 145	92.36 148

IterMVS-LS		84.73 119	84.98 115	83.96 145	87.35 193	63.66 184	83.25 165	89.88 153	76.06 122	89.62 101	92.37 120	73.40 178	92.52 140	78.16 118	94.77 143	95.69 57
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1		84.16 138	84.38 136	83.52 160	87.33 194	61.71 219	82.79 176	89.73 156	71.89 178	89.64 98	91.11 153	72.72 190	93.10 123	80.40 93	93.79 163	95.75 54

v1141		84.16 138	84.38 136	83.52 160	87.32 195	61.70 221	82.79 176	89.74 154	71.90 176	89.64 98	91.12 151	72.68 193	93.10 123	80.39 95	93.80 162	95.75 54

divwei89l23v2f112		84.16 138	84.38 136	83.52 160	87.32 195	61.70 221	82.79 176	89.74 154	71.90 176	89.64 98	91.12 151	72.68 193	93.10 123	80.40 93	93.81 161	95.75 54

MIMVSNet		71.09 259	71.59 255	69.57 283	87.23 197	50.07 306	78.91 241	71.83 291	60.20 263	71.26 289	91.76 133	55.08 273	76.09 292	41.06 317	87.02 257	82.54 275

Effi-MVS+		83.90 148	84.01 144	83.57 158	87.22 198	65.61 171	86.55 105	92.40 73	78.64 90	81.34 221	84.18 263	83.65 61	92.93 130	74.22 144	87.87 248	92.17 156

BH-RMVSNet		80.53 189	80.22 190	81.49 193	87.19 199	66.21 167	77.79 252	86.23 205	74.21 142	83.69 193	88.50 204	73.25 184	90.75 182	63.18 228	87.90 247	87.52 223

Effi-MVS+-dtu		85.82 102	83.38 151	93.14 3	87.13 200	91.15 3	87.70 79	88.42 172	74.57 139	83.56 197	85.65 240	78.49 112	94.21 63	72.04 167	92.88 187	94.05 98

mvs-test1		84.55 123	82.12 167	91.84 17	87.13 200	89.54 5	85.05 121	88.42 172	74.57 139	80.60 227	82.98 275	78.49 112	93.98 73	72.04 167	89.77 228	92.00 158

v2v482		84.09 141	84.24 141	83.62 156	87.13 200	61.40 225	82.71 180	89.71 157	72.19 172	89.55 105	91.41 141	70.70 209	93.20 117	81.02 83	93.76 164	96.25 41

jason		77.42 210	75.75 224	82.43 181	87.10 203	69.27 147	77.99 249	81.94 237	51.47 303	77.84 245	85.07 253	60.32 244	89.00 216	70.74 175	89.27 232	89.03 208
jason: jason.
PS-MVSNAJ		77.04 214	76.53 217	78.56 226	87.09 204	61.40 225	75.26 273	87.13 195	61.25 257	74.38 274	77.22 309	76.94 130	90.94 175	64.63 219	84.83 278	83.35 265

xiu_mvs_v2_base		77.19 212	76.75 211	78.52 227	87.01 205	61.30 227	75.55 272	87.12 197	61.24 258	74.45 272	78.79 303	77.20 124	90.93 176	64.62 220	84.80 279	83.32 266

thres600view7		75.97 228	75.35 228	77.85 237	87.01 205	51.84 291	80.45 222	73.26 281	75.20 134	83.10 201	86.31 236	45.54 300	89.05 215	55.03 276	92.24 197	92.66 135

BH-w/o		76.57 222	76.07 221	78.10 232	86.88 207	65.92 169	77.63 253	86.33 204	65.69 224	80.89 224	79.95 299	68.97 215	90.74 183	53.01 284	85.25 272	77.62 306

MAR-MVS		80.24 193	78.74 200	84.73 124	86.87 208	78.18 76	85.75 112	87.81 184	65.67 225	77.84 245	78.50 304	73.79 167	90.53 189	61.59 237	90.87 215	85.49 244
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
testing_2		84.36 130	84.64 128	83.50 163	86.74 209	63.97 182	84.56 128	90.31 137	66.22 218	91.62 63	94.55 61	75.88 140	91.95 149	77.02 131	94.89 135	94.56 81

QAPM		82.59 165	82.59 162	82.58 176	86.44 210	66.69 165	89.94 43	90.36 134	67.97 206	84.94 172	92.58 114	72.71 191	92.18 145	70.63 177	87.73 250	88.85 211

PAPM		71.77 254	70.06 265	76.92 246	86.39 211	53.97 274	76.62 261	86.62 202	53.44 289	63.97 318	84.73 259	57.79 260	92.34 142	39.65 319	81.33 298	84.45 252

GBi-Net		82.02 173	82.07 168	81.85 188	86.38 212	61.05 231	86.83 94	88.27 176	72.43 165	86.00 159	95.64 33	63.78 232	90.68 185	65.95 210	93.34 174	93.82 105

test1		82.02 173	82.07 168	81.85 188	86.38 212	61.05 231	86.83 94	88.27 176	72.43 165	86.00 159	95.64 33	63.78 232	90.68 185	65.95 210	93.34 174	93.82 105

FMVSNet2		81.31 178	81.61 174	80.41 205	86.38 212	58.75 249	83.93 141	86.58 203	72.43 165	87.65 133	92.98 102	63.78 232	90.22 196	66.86 204	93.92 159	92.27 152

3Dnovator	80.37 7	84.80 118	84.71 122	85.06 115	86.36 215	74.71 104	88.77 66	90.00 151	75.65 129	84.96 170	93.17 98	74.06 161	91.19 169	78.28 117	91.09 207	89.29 205

Anonymous20231206		71.38 258	71.88 253	69.88 279	86.31 216	54.37 272	70.39 294	74.62 272	52.57 294	76.73 252	88.76 200	59.94 247	72.06 301	44.35 312	93.23 181	83.23 268

API-MVS		82.28 169	82.61 161	81.30 195	86.29 217	69.79 142	88.71 67	87.67 185	78.42 93	82.15 209	84.15 265	77.98 116	91.59 160	65.39 215	92.75 189	82.51 277

thres400		75.14 231	74.23 235	77.86 236	86.24 218	52.12 288	79.24 238	73.87 276	73.34 152	81.82 214	84.60 261	46.02 295	88.80 219	51.98 288	90.99 211	92.66 135

testmv		70.47 263	70.70 258	69.77 281	86.22 219	53.89 276	67.32 304	71.91 290	63.32 239	78.16 244	89.47 192	56.12 267	73.10 299	36.43 325	87.33 253	82.33 279

UGNet		82.78 162	81.64 173	86.21 97	86.20 220	76.24 98	86.86 92	85.68 211	77.07 115	73.76 276	92.82 107	69.64 210	91.82 155	69.04 190	93.69 166	90.56 188
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
CANet		83.79 149	82.85 157	86.63 85	86.17 221	72.21 124	83.76 148	91.43 105	77.24 112	74.39 273	87.45 224	75.36 143	95.42 29	77.03 130	92.83 188	92.25 154

TR-MVS		76.77 220	75.79 222	79.72 212	86.10 222	65.79 170	77.14 257	83.02 229	65.20 230	81.40 219	82.10 286	66.30 222	90.73 184	55.57 271	85.27 271	82.65 272

LCM-MVSNet-Re		83.48 155	85.06 112	78.75 223	85.94 223	55.75 266	80.05 225	94.27 13	76.47 119	96.09 5	94.54 63	83.31 65	89.75 205	59.95 245	94.89 135	90.75 182

Fast-Effi-MVS+-dtu		82.54 166	81.41 176	85.90 104	85.60 224	76.53 94	83.07 169	89.62 159	73.02 160	79.11 242	83.51 269	80.74 97	90.24 195	68.76 192	89.29 231	90.94 175

v148		82.31 168	82.48 164	81.81 191	85.59 225	59.66 241	81.47 211	86.02 208	72.85 161	88.05 128	90.65 171	70.73 208	90.91 178	75.15 139	91.79 201	94.87 77

MVSFormer		82.23 170	81.57 175	84.19 141	85.54 226	69.26 148	91.98 25	90.08 148	71.54 179	76.23 257	85.07 253	58.69 255	94.27 59	86.26 27	88.77 236	89.03 208

lupinMVS		76.37 225	74.46 233	82.09 182	85.54 226	69.26 148	76.79 258	80.77 244	50.68 310	76.23 257	82.82 279	58.69 255	88.94 217	69.85 181	88.77 236	88.07 215

TinyColmap		81.25 181	82.34 166	77.99 234	85.33 228	60.68 235	82.32 189	88.33 174	71.26 181	86.97 145	92.22 124	77.10 127	86.98 237	62.37 229	95.17 127	86.31 235

PAPR		78.84 200	78.10 203	81.07 199	85.17 229	60.22 238	82.21 194	90.57 128	62.51 247	75.32 266	84.61 260	74.99 148	92.30 143	59.48 249	88.04 246	90.68 184

DI_MVS_plusplus_test		81.27 180	81.26 177	81.29 196	84.98 230	61.65 223	81.98 199	87.25 191	63.56 237	87.56 135	89.60 190	73.62 170	91.83 154	72.20 166	90.59 224	90.38 193

pmmvs4		74.92 236	72.98 244	80.73 203	84.95 231	71.71 133	76.23 266	77.59 255	52.83 292	77.73 248	86.38 233	56.35 265	84.97 258	57.72 261	87.05 256	85.51 243

Test4		81.31 178	81.13 180	81.88 187	84.89 232	63.05 196	82.37 187	90.50 129	62.75 245	89.00 113	88.29 211	67.55 218	91.68 158	73.55 153	91.24 206	90.89 177

Patchmatch-RL test		74.48 238	73.68 237	76.89 247	84.83 233	66.54 166	72.29 288	69.16 307	57.70 269	86.76 146	86.33 234	45.79 299	82.59 274	69.63 183	90.65 222	81.54 292

test_normal		81.23 182	81.16 179	81.43 194	84.77 234	61.99 218	81.46 212	86.95 201	63.16 242	87.22 138	89.63 189	73.62 170	91.65 159	72.92 161	90.70 219	90.65 186

XXY-MVS		74.44 240	76.19 220	69.21 284	84.61 235	52.43 287	71.70 290	77.18 257	60.73 261	80.60 227	90.96 160	75.44 141	69.35 308	56.13 267	88.33 240	85.86 240

cascas		76.29 226	74.81 229	80.72 204	84.47 236	62.94 198	73.89 282	87.34 188	55.94 277	75.16 268	76.53 312	63.97 230	91.16 170	65.00 216	90.97 212	88.06 216

PVSNet_BlendedMVS		78.80 201	77.84 204	81.65 192	84.43 237	63.41 187	79.49 234	90.44 131	61.70 255	75.43 264	87.07 228	69.11 213	91.44 164	60.68 242	92.24 197	90.11 199

PVSNet_Blended		76.49 224	75.40 226	79.76 211	84.43 237	63.41 187	75.14 274	90.44 131	57.36 271	75.43 264	78.30 305	69.11 213	91.44 164	60.68 242	87.70 251	84.42 253

OpenMVS	76.72 13	81.98 175	82.00 170	81.93 184	84.42 239	68.22 156	88.50 70	89.48 161	66.92 213	81.80 215	91.86 127	72.59 195	90.16 198	71.19 171	91.25 205	87.40 225

OpenMVS_ROB	70.19 17	77.77 208	77.46 206	78.71 224	84.39 240	61.15 229	81.18 217	82.52 231	62.45 249	83.34 199	87.37 225	66.20 223	88.66 223	64.69 218	85.02 275	86.32 234

Regformer-3		85.06 111	84.67 124	86.22 95	84.27 241	73.43 111	84.07 135	85.26 215	80.77 63	88.62 120	85.48 243	80.56 99	90.39 192	81.99 75	91.04 209	94.85 79

Regformer-4		86.41 88	85.71 103	88.52 61	84.27 241	77.57 81	84.07 135	88.00 181	82.82 42	89.84 92	85.48 243	82.06 79	92.77 134	83.83 55	91.04 209	95.22 74

DELS-MVS		81.44 177	81.25 178	82.03 183	84.27 241	62.87 200	76.47 264	92.49 72	70.97 184	81.64 217	83.83 266	75.03 147	92.70 135	74.29 143	92.22 199	90.51 190
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
Gipuma		84.44 126	86.33 92	78.78 222	84.20 244	73.57 110	89.55 51	90.44 131	84.24 29	84.38 185	94.89 51	76.35 139	80.40 282	76.14 134	96.80 74	82.36 278
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Regformer-1		86.00 97	85.50 107	87.49 77	84.18 245	76.90 89	83.52 155	87.94 183	82.18 50	89.19 110	85.07 253	82.28 77	91.89 152	82.40 71	92.72 190	93.69 111

Regformer-2		86.74 83	86.08 98	88.73 59	84.18 245	79.20 69	83.52 155	89.33 163	83.33 35	89.92 90	85.07 253	83.23 66	93.16 120	83.39 57	92.72 190	93.83 103

EI-MVSNet-Vis-set		85.12 110	84.53 131	86.88 82	84.01 247	72.76 115	83.91 142	85.18 217	80.44 64	88.75 117	85.49 242	80.08 102	91.92 150	82.02 74	90.85 216	95.97 49

semantic-postprocess				84.34 136	83.93 248	69.66 144		81.09 242	72.43 165	86.47 156	90.19 180	57.56 261	93.15 122	77.45 125	86.39 263	90.22 195

MSDG		80.06 196	79.99 194	80.25 206	83.91 249	68.04 157	77.51 255	89.19 164	77.65 101	81.94 211	83.45 271	76.37 138	86.31 245	63.31 227	86.59 260	86.41 233

EI-MVSNet-UG-set		85.04 112	84.44 133	86.85 83	83.87 250	72.52 118	83.82 144	85.15 218	80.27 68	88.75 117	85.45 246	79.95 104	91.90 151	81.92 76	90.80 217	96.13 43

USDC		76.63 221	76.73 212	76.34 252	83.46 251	57.20 256	80.02 226	88.04 180	52.14 298	83.65 196	91.25 143	63.24 235	86.65 242	54.66 278	94.11 154	85.17 245

HY-MVS	64.64 18	73.03 246	72.47 250	74.71 261	83.36 252	54.19 273	82.14 197	81.96 235	56.76 276	69.57 297	86.21 237	60.03 246	84.83 261	49.58 296	82.65 291	85.11 246

EI-MVSNet		82.61 164	82.42 165	83.20 166	83.25 253	63.66 184	83.50 158	85.07 219	76.06 122	86.55 150	85.10 251	73.41 176	90.25 193	78.15 119	90.67 220	95.68 58

CVMVSNet		72.62 249	71.41 256	76.28 253	83.25 253	60.34 237	83.50 158	79.02 251	37.77 332	76.33 255	85.10 251	49.60 285	87.41 232	70.54 178	77.54 312	81.08 299

V42		83.47 156	83.37 152	83.75 150	83.16 255	63.33 190	81.31 213	90.23 146	69.51 194	90.91 74	90.81 165	74.16 160	92.29 144	80.06 99	90.22 225	95.62 60

EU-MVSNet		75.12 233	74.43 234	77.18 244	83.11 256	59.48 243	85.71 114	82.43 232	39.76 331	85.64 164	88.76 200	44.71 313	87.88 228	73.86 150	85.88 266	84.16 256

FMVSNet3		78.80 201	78.55 201	79.57 216	82.89 257	56.89 259	81.76 205	85.77 210	69.04 199	86.00 159	90.44 175	51.75 280	90.09 202	65.95 210	93.34 174	91.72 164

test1235678		65.57 288	65.73 285	65.06 300	82.84 258	50.90 297	62.90 312	69.26 305	57.17 274	72.36 283	83.04 274	46.02 295	70.10 305	32.79 330	85.24 273	74.19 313

MVS_Test		82.47 167	83.22 153	80.22 207	82.62 259	57.75 253	82.54 184	91.96 85	71.16 182	82.89 203	92.52 116	77.41 123	90.50 190	80.04 100	87.84 249	92.40 146

LF4IMVS		82.75 163	81.93 171	85.19 113	82.08 260	80.15 61	85.53 115	88.76 168	68.01 204	85.58 165	87.75 219	71.80 203	86.85 239	74.02 147	93.87 160	88.58 212

PVSNet	58.17 21	66.41 284	65.63 286	68.75 287	81.96 261	49.88 307	62.19 314	72.51 286	51.03 306	68.04 302	75.34 316	50.84 281	74.77 296	45.82 310	82.96 287	81.60 291

GA-MVS		75.83 229	74.61 230	79.48 218	81.87 262	59.25 245	73.42 285	82.88 230	68.68 201	79.75 236	81.80 289	50.62 282	89.46 207	66.85 205	85.64 268	89.72 201

MS-PatchMatch		70.93 260	70.22 263	73.06 269	81.85 263	62.50 209	73.82 283	77.90 253	52.44 295	75.92 260	81.27 293	55.67 269	81.75 277	55.37 273	77.70 310	74.94 311

Patchmatch-test1		72.75 248	72.61 247	73.19 267	81.62 264	55.86 264	78.89 242	71.37 295	61.73 253	74.93 269	82.15 285	60.46 243	81.80 276	59.68 247	82.63 293	81.92 286

FMVSNet5		72.10 253	71.69 254	73.32 265	81.57 265	53.02 282	76.77 259	78.37 252	63.31 240	76.37 254	91.85 128	36.68 327	78.98 286	47.87 303	92.45 193	87.95 219

CANet_DTU		77.81 207	77.05 208	80.09 208	81.37 266	59.90 240	83.26 164	88.29 175	69.16 197	67.83 304	83.72 267	60.93 240	89.47 206	69.22 188	89.70 229	90.88 178

ANet_high		83.17 160	85.68 104	75.65 257	81.24 267	45.26 316	79.94 227	92.91 58	83.83 33	91.33 68	96.88 10	80.25 101	85.92 249	68.89 191	95.89 107	95.76 53

new-patchmatchnet		70.10 266	73.37 240	60.29 312	81.23 268	16.95 339	59.54 318	74.62 272	62.93 243	80.97 222	87.93 217	62.83 236	71.90 302	55.24 274	95.01 132	92.00 158

test20.03		73.75 243	74.59 232	71.22 278	81.11 269	51.12 295	70.15 295	72.10 288	70.42 187	80.28 234	91.50 139	64.21 229	74.72 298	46.96 307	94.58 148	87.82 222

MVS		73.21 245	72.59 248	75.06 259	80.97 270	60.81 234	81.64 208	85.92 209	46.03 322	71.68 287	77.54 306	68.47 216	89.77 204	55.70 270	85.39 269	74.60 312

N_pmnet		70.20 264	68.80 271	74.38 262	80.91 271	84.81 35	59.12 321	76.45 263	55.06 281	75.31 267	82.36 284	55.74 268	54.82 333	47.02 306	87.24 255	83.52 261

IterMVS		76.91 215	76.34 219	78.64 225	80.91 271	64.03 180	76.30 265	79.03 250	64.88 234	83.11 200	89.16 195	59.90 248	84.46 263	68.61 195	85.15 274	87.42 224
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WTY-MVS		67.91 278	68.35 273	66.58 295	80.82 273	48.12 310	65.96 308	72.60 284	53.67 288	71.20 290	81.68 291	58.97 254	69.06 310	48.57 299	81.67 295	82.55 274

IB-MVS	62.13 19	71.64 255	68.97 269	79.66 215	80.80 274	62.26 217	73.94 281	76.90 259	63.27 241	68.63 300	76.79 310	33.83 330	91.84 153	59.28 250	87.26 254	84.88 248
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
testgi		72.36 251	74.61 230	65.59 297	80.56 275	42.82 324	68.29 299	73.35 280	66.87 214	81.84 213	89.93 184	72.08 200	66.92 316	46.05 309	92.54 192	87.01 229

1314		73.22 244	72.56 249	75.20 258	80.41 276	57.84 251	81.64 208	85.36 214	51.68 301	73.10 280	76.65 311	61.45 239	85.19 256	63.54 224	79.21 307	82.59 273

CR-MVSNet		74.00 241	73.04 243	76.85 248	79.58 277	62.64 203	82.58 181	76.90 259	50.50 311	75.72 262	92.38 117	48.07 289	84.07 265	68.72 194	82.91 289	83.85 257

RPMNet		76.06 227	75.79 222	76.85 248	79.58 277	62.64 203	82.58 181	71.75 293	74.80 136	75.72 262	92.59 112	48.69 287	84.07 265	73.48 154	82.91 289	83.85 257

UnsupCasMVSNet_bld		69.21 274	69.68 266	67.82 291	79.42 279	51.15 294	67.82 303	75.79 264	54.15 285	77.47 249	85.36 250	59.26 253	70.64 304	48.46 300	79.35 305	81.66 290

PatchT		70.52 262	72.76 245	63.79 303	79.38 280	33.53 332	77.63 253	65.37 318	73.61 147	71.77 286	92.79 110	44.38 314	75.65 295	64.53 221	85.37 270	82.18 281

Patchmtry		76.56 223	77.46 206	73.83 264	79.37 281	46.60 313	82.41 186	76.90 259	73.81 146	85.56 166	92.38 117	48.07 289	83.98 267	63.36 226	95.31 123	90.92 176

mvs_anonymous		78.13 204	78.76 199	76.23 254	79.24 282	50.31 305	78.69 244	84.82 224	61.60 256	83.09 202	92.82 107	73.89 166	87.01 235	68.33 197	86.41 262	91.37 171

MVS-HIRNet		61.16 299	62.92 294	55.87 316	79.09 283	35.34 331	71.83 289	57.98 332	46.56 320	59.05 328	91.14 150	49.95 284	76.43 291	38.74 322	71.92 322	55.84 330

MDA-MVSNet-bldmvs		77.47 209	76.90 210	79.16 219	79.03 284	64.59 175	66.58 307	75.67 266	73.15 158	88.86 114	88.99 198	66.94 220	81.23 279	64.71 217	88.22 245	91.64 166

tpm2		68.45 276	66.83 280	73.30 266	78.93 285	48.50 308	79.76 228	71.76 292	47.50 318	69.92 296	83.60 268	42.07 320	88.40 224	48.44 301	79.51 303	83.01 271

testus		62.33 293	63.03 293	60.20 313	78.78 286	40.74 325	59.14 319	69.80 304	49.26 315	71.41 288	74.72 318	52.33 279	63.52 325	29.84 332	82.01 294	76.36 308

tpm		67.95 277	68.08 276	67.55 292	78.74 287	43.53 322	75.60 270	67.10 315	54.92 282	72.23 284	88.10 213	42.87 318	75.97 293	52.21 286	80.95 301	83.15 269

diffmvs		79.20 199	79.04 197	79.69 213	78.64 288	58.90 246	81.79 204	87.61 186	65.07 232	73.65 278	89.80 186	73.10 186	87.79 229	75.02 141	86.63 259	92.38 147

tpmp4_e23		69.43 271	67.33 278	75.72 256	78.53 289	52.75 283	82.13 198	74.91 269	49.23 316	66.37 307	84.17 264	41.28 321	88.67 222	49.73 295	79.63 302	85.75 241

no-one		71.52 257	70.43 262	74.81 260	78.45 290	63.41 187	57.73 324	77.03 258	51.46 304	77.17 251	90.33 177	54.96 274	80.35 283	47.41 304	99.29 2	80.68 303

1111		61.71 295	63.77 291	55.55 318	78.05 291	25.74 336	60.62 315	67.52 308	66.09 219	74.68 270	86.50 231	16.00 342	59.22 331	38.79 320	85.65 267	81.70 288

.test1245		48.02 312	54.41 311	28.84 325	78.05 291	25.74 336	60.62 315	67.52 308	66.09 219	74.68 270	86.50 231	16.00 342	59.22 331	38.79 320	1.47 337	1.55 336

MDTV_nov1_ep13			68.29 275		78.03 293	43.87 321	74.12 280	72.22 287	52.17 296	67.02 306	85.54 241	45.36 303	80.85 280	55.73 268	84.42 281

EPNet_dtu		72.87 247	71.33 257	77.49 243	77.72 294	60.55 236	82.35 188	75.79 264	66.49 216	58.39 331	81.06 295	53.68 276	85.98 248	53.55 282	92.97 186	85.95 238
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchmatchNet		69.71 270	68.83 270	72.33 274	77.66 295	53.60 277	79.29 236	69.99 303	57.66 270	72.53 282	82.93 278	46.45 293	80.08 285	60.91 241	72.09 321	83.31 267
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sss		66.92 281	67.26 279	65.90 296	77.23 296	51.10 296	64.79 309	71.72 294	52.12 299	70.13 295	80.18 297	57.96 258	65.36 323	50.21 291	81.01 300	81.25 296

CostFormer		69.98 269	68.68 272	73.87 263	77.14 297	50.72 303	79.26 237	74.51 274	51.94 300	70.97 292	84.75 258	45.16 311	87.49 231	55.16 275	79.23 306	83.40 264

tpm cat1		66.76 282	65.21 287	71.42 277	77.09 298	50.62 304	78.01 248	73.68 278	44.89 324	68.64 298	79.00 302	45.51 301	82.42 275	49.91 293	70.15 325	81.23 298

pmmvs5		70.73 261	70.07 264	72.72 271	77.03 299	52.73 284	74.14 279	75.65 267	50.36 312	72.17 285	85.37 249	55.42 271	80.67 281	52.86 285	87.59 252	84.77 249

EPNet		80.37 190	78.41 202	86.23 94	76.75 300	73.28 112	87.18 88	77.45 256	76.24 121	68.14 301	88.93 199	65.41 227	93.85 78	69.47 184	96.12 98	91.55 169
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 280x420		59.08 303	56.52 308	66.76 294	76.51 301	64.39 178	49.62 331	59.00 328	43.86 326	55.66 334	68.41 326	35.55 329	68.21 312	43.25 313	76.78 314	67.69 322

UnsupCasMVSNet_eth		71.63 256	72.30 251	69.62 282	76.47 302	52.70 285	70.03 296	80.97 243	59.18 265	79.36 240	88.21 212	60.50 242	69.12 309	58.33 258	77.62 311	87.04 228

test-LLR		67.21 280	66.74 281	68.63 288	76.45 303	55.21 268	67.89 300	67.14 313	62.43 250	65.08 314	72.39 320	43.41 315	69.37 306	61.00 239	84.89 276	81.31 294

test-mter		65.00 289	63.79 290	68.63 288	76.45 303	55.21 268	67.89 300	67.14 313	50.98 307	65.08 314	72.39 320	28.27 337	69.37 306	61.00 239	84.89 276	81.31 294

gg-mvs-nofinetune		68.96 275	69.11 268	68.52 290	76.12 305	45.32 315	83.59 151	55.88 333	86.68 20	64.62 317	97.01 7	30.36 334	83.97 268	44.78 311	82.94 288	76.26 309

CMPMVS	59.41 20	75.12 233	73.57 238	79.77 210	75.84 306	67.22 160	81.21 216	82.18 233	50.78 308	76.50 253	87.66 221	55.20 272	82.99 272	62.17 231	90.64 223	89.09 207
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test12356		54.91 309	57.14 307	48.22 322	75.83 307	17.47 338	52.31 330	69.20 306	51.66 302	60.11 324	75.40 315	29.77 336	62.62 329	27.64 333	72.37 320	64.59 323

PNet_i23d		52.13 310	51.24 312	54.79 319	75.56 308	45.26 316	54.54 327	52.55 336	66.95 212	57.19 332	65.82 329	13.15 344	63.40 326	36.39 326	39.04 335	55.71 331

wuyk23d		75.13 232	79.30 196	62.63 304	75.56 308	75.18 102	80.89 219	73.10 282	75.06 135	94.76 15	95.32 40	87.73 31	52.85 334	34.16 328	97.11 64	59.85 327

Patchmatch-test		65.91 286	67.38 277	61.48 309	75.51 310	43.21 323	68.84 297	63.79 320	62.48 248	72.80 281	83.42 272	44.89 312	59.52 330	48.27 302	86.45 261	81.70 288

new_pmnet		55.69 307	57.66 306	49.76 320	75.47 311	30.59 333	59.56 317	51.45 337	43.62 327	62.49 319	75.48 314	40.96 323	49.15 336	37.39 324	72.52 319	69.55 320

gm-plane-assit					75.42 312	44.97 319			52.17 296		72.36 322		87.90 227	54.10 280

PatchFormer-LS_test		67.91 278	66.49 284	72.17 275	75.29 313	51.85 290	75.68 268	73.62 279	57.23 273	68.64 298	68.13 328	42.19 319	82.76 273	64.06 222	73.51 318	81.89 287

MVSTER		77.09 213	75.70 225	81.25 197	75.27 314	61.08 230	77.49 256	85.07 219	60.78 260	86.55 150	88.68 202	43.14 317	90.25 193	73.69 151	90.67 220	92.42 144

PVSNet_0	51.08 22	56.10 306	54.97 310	59.48 314	75.12 315	53.28 281	55.16 325	61.89 323	44.30 325	59.16 327	62.48 332	54.22 275	65.91 321	35.40 327	47.01 333	59.25 328

test0.0.03 1		64.66 290	64.36 289	65.57 298	75.03 316	46.89 312	64.69 310	61.58 326	62.43 250	71.18 291	77.54 306	43.41 315	68.47 311	40.75 318	82.65 291	81.35 293

DWT-MVSNet_test		66.43 283	64.37 288	72.63 272	74.86 317	50.86 298	76.52 262	72.74 283	54.06 286	65.50 311	68.30 327	32.13 332	84.84 260	61.63 236	73.59 317	82.19 280

tpmvs		70.16 265	69.56 267	71.96 276	74.71 318	48.13 309	79.63 229	75.45 268	65.02 233	70.26 294	81.88 288	45.34 304	85.68 252	58.34 257	75.39 315	82.08 282

MDA-MVSNet_test_wron		70.05 268	70.44 260	68.88 286	73.84 319	53.47 278	58.93 323	67.28 311	58.43 266	87.09 142	85.40 247	59.80 250	67.25 314	59.66 248	83.54 284	85.92 239

YYNet1		70.06 267	70.44 260	68.90 285	73.76 320	53.42 280	58.99 322	67.20 312	58.42 267	87.10 141	85.39 248	59.82 249	67.32 313	59.79 246	83.50 285	85.96 237

GG-mvs-BLEND				67.16 293	73.36 321	46.54 314	84.15 134	55.04 334		58.64 330	61.95 333	29.93 335	83.87 269	38.71 323	76.92 313	71.07 318

test2356		56.69 305	55.15 309	61.32 310	73.20 322	44.11 320	54.95 326	62.52 321	48.75 317	62.45 320	68.42 325	21.10 341	65.67 322	26.86 334	78.08 309	74.19 313

JIA-IIPM		69.41 273	66.64 283	77.70 238	73.19 323	71.24 136	75.67 269	65.56 317	70.42 187	65.18 313	92.97 103	33.64 331	83.06 271	53.52 283	69.61 328	78.79 305

ADS-MVSNet2		65.87 287	63.64 292	72.55 273	73.16 324	56.92 258	67.10 305	74.81 271	49.74 313	66.04 309	82.97 276	46.71 291	77.26 288	42.29 314	69.96 326	83.46 262

ADS-MVSNet		61.90 294	62.19 296	61.03 311	73.16 324	36.42 330	67.10 305	61.75 324	49.74 313	66.04 309	82.97 276	46.71 291	63.21 327	42.29 314	69.96 326	83.46 262

DSMNet-mixed		60.98 301	61.61 298	59.09 315	72.88 326	45.05 318	74.70 276	46.61 339	26.20 334	65.34 312	90.32 178	55.46 270	63.12 328	41.72 316	81.30 299	69.09 321

tpmrst		66.28 285	66.69 282	65.05 301	72.82 327	39.33 327	78.20 247	70.69 301	53.16 291	67.88 303	80.36 296	48.18 288	74.75 297	58.13 259	70.79 323	81.08 299

TESTMET0.1,1		61.29 298	60.32 302	64.19 302	72.06 328	51.30 292	67.89 300	62.09 322	45.27 323	60.65 323	69.01 323	27.93 338	64.74 324	56.31 266	81.65 297	76.53 307

dp		60.70 302	60.29 303	61.92 307	72.04 329	38.67 329	70.83 291	64.08 319	51.28 305	60.75 322	77.28 308	36.59 328	71.58 303	47.41 304	62.34 332	75.52 310

pmmvs3		62.47 291	60.02 304	69.80 280	71.58 330	64.00 181	70.52 293	58.44 330	39.77 330	66.05 308	75.84 313	27.10 339	72.28 300	46.15 308	84.77 280	73.11 315

LP		69.42 272	68.30 274	72.77 270	71.48 331	56.84 260	73.66 284	74.84 270	63.52 238	70.95 293	83.35 273	49.55 286	77.15 290	57.13 263	70.21 324	84.33 254

EPMVS		62.47 291	62.63 295	62.01 305	70.63 332	38.74 328	74.76 275	52.86 335	53.91 287	67.71 305	80.01 298	39.40 325	66.60 318	55.54 272	68.81 330	80.68 303

E-PMN		61.59 297	61.62 297	61.49 308	66.81 333	55.40 267	53.77 328	60.34 327	66.80 215	58.90 329	65.50 330	40.48 324	66.12 320	55.72 269	86.25 264	62.95 325

testpf		58.55 304	61.58 299	49.48 321	66.03 334	40.05 326	74.40 278	58.07 331	64.72 235	59.36 326	72.67 319	22.76 340	66.92 316	67.07 203	69.15 329	41.46 332

EMVS		61.10 300	60.81 300	61.99 306	65.96 335	55.86 264	53.10 329	58.97 329	67.06 211	56.89 333	63.33 331	40.98 322	67.03 315	54.79 277	86.18 265	63.08 324

PMMVS		61.65 296	60.38 301	65.47 299	65.40 336	69.26 148	63.97 311	61.73 325	36.80 333	60.11 324	68.43 324	59.42 251	66.35 319	48.97 298	78.57 308	60.81 326

PMMVS2		55.64 308	59.27 305	44.74 323	64.30 337	12.32 340	40.60 332	49.79 338	53.19 290	65.06 316	84.81 257	53.60 277	49.76 335	32.68 331	89.41 230	72.15 316

MVE	40.22 23	51.82 311	50.47 313	55.87 316	62.66 338	51.91 289	31.61 334	39.28 340	40.65 329	50.76 335	74.98 317	56.24 266	44.67 337	33.94 329	64.11 331	71.04 319
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				24.13 326	32.95 339	29.49 334		21.63 343	12.07 335	37.95 336	45.07 334	30.84 333	19.21 338	17.94 336	33.06 336	23.69 333

tmp_tt		20.25 315	24.50 316	7.49 327	4.47 340	8.70 341	34.17 333	25.16 342	1.00 336	32.43 337	18.49 335	39.37 326	9.21 339	21.64 335	43.75 334	4.57 334

testmvs		5.91 319	7.65 320	0.72 329	1.20 341	0.37 343	59.14 319	0.67 345	0.49 338	1.11 338	2.76 339	0.94 346	0.24 341	1.02 338	1.47 337	1.55 336

test123		6.27 318	8.08 319	0.84 328	1.11 342	0.57 342	62.90 312	0.82 344	0.54 337	1.07 339	2.75 340	1.26 345	0.30 340	1.04 337	1.26 339	1.66 335

cdsmvs_eth3d_5k		20.81 314	27.75 315	0.00 330	0.00 343	0.00 344	0.00 335	85.44 213	0.00 339	0.00 340	82.82 279	81.46 90	0.00 342	0.00 339	0.00 340	0.00 338

pcd_1.5k_mvsjas		6.41 317	8.55 318	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	0.00 341	76.94 130	0.00 342	0.00 339	0.00 340	0.00 338

sosnet-low-res		0.00 320	0.00 321	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	0.00 341	0.00 347	0.00 342	0.00 339	0.00 340	0.00 338

sosnet		0.00 320	0.00 321	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	0.00 341	0.00 347	0.00 342	0.00 339	0.00 340	0.00 338

uncertanet		0.00 320	0.00 321	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	0.00 341	0.00 347	0.00 342	0.00 339	0.00 340	0.00 338

Regformer		0.00 320	0.00 321	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	0.00 341	0.00 347	0.00 342	0.00 339	0.00 340	0.00 338

ab-mvs-re		6.65 316	8.87 317	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	79.80 300	0.00 347	0.00 342	0.00 339	0.00 340	0.00 338

uanet		0.00 320	0.00 321	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	0.00 341	0.00 347	0.00 342	0.00 339	0.00 340	0.00 338

ESAPD								93.79 28

sam_mvs1												46.11 294

sam_mvs												45.92 298

MTGPA								91.81 89

test_post1							78.85 243				3.13 337	45.19 306	80.13 284	58.11 260

test_post											3.10 338	45.43 302	77.22 289

patchmatchnet-post											81.71 290	45.93 297	87.01 235

MTMP								33.14 341

test9_res														80.83 87	96.45 85	90.57 187

agg_prior2														79.68 104	96.16 94	90.22 195

test_prior4						78.97 71	84.59 127

test_prior2							83.37 161		75.43 131	84.58 181	91.57 136	81.92 85		79.54 106	96.97 67

旧先验2							81.73 206		56.88 275	86.54 155			84.90 259	72.81 162

新几何2							81.72 207

无先验							82.81 175	85.62 212	58.09 268				91.41 166	67.95 200		84.48 251

原ACMM2							82.26 193

testdata2													86.43 244	63.52 225

segment_acmp												81.94 82

testdata1							79.62 230		73.95 145

plane_prior5								93.61 31					95.22 37	80.78 88	95.83 110	94.46 86

plane_prior4											92.95 104

plane_prior3						76.85 90			77.79 99	86.55 150

plane_prior2							89.45 56		79.44 77

plane_prior						76.42 95	87.15 89		75.94 126						95.03 131

n2								0.00 346

nn								0.00 346

door-mid								74.45 275

test11								91.46 102

door								72.57 285

HQP5-MVS						70.66 139

BP-MVS														77.30 127

HQP4-MVS										80.56 229			94.61 53			93.56 116

HQP3-MVS								92.68 66							94.47 150

HQP2-MVS												72.10 198

MDTV_nov1_ep13_2view						27.60 335	70.76 292		46.47 321	61.27 321		45.20 305		49.18 297		83.75 259

ACMMP++_ref															95.74 114

ACMMP++															97.35 58

Test By Simon												79.09 108

High-resolution multi-view benchmark