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All Projects → ahojnnes → Local Feature Evaluation

ahojnnes / Local Feature Evaluation

Comparative Evaluation of Hand-Crafted and Learned Local Features

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Comparative Evaluation of Hand-Crafted and Learned Local Features

This repository contains the instructions and the code for evaluating feature descriptors on our image-based reconstruction benchmark. The details of our local feature benchmark can be found in our paper:

"Comparative Evaluation of Hand-Crafted and Learned Local Features".
J.L. Schönberger, H. Hardmeier, T. Sattler and M. Pollefeys. CVPR 2017.

Paper, Supplementary, Bibtex

You might also be interested in the HPatches benchmark by Balntas and Lenc et al. presented at CVPR 2017.

Benchmark Results

This table lists the latest benchmark results. Note that the results differ from the original paper, since they were updated with the latest COLMAP version. If you want to submit your own results, please open a new issue or pull request for this repository. Note that the below table extends to the right and alternatively can be viewed in a code or text editor.

Metrics:

Dataset Method # Images # Reg. Images # Sparse Points # Observations Track Length Obs. Per Image Reproj. Error [px] # Dense Points Dense Error [2cm] Dense Error [10cm] Mean Pose Error [m] Median Pose Error [m] # Inlier Pairs # Inlier Matches
Fountain SIFT 11 11 14722 70631 4.79765 6421.00 0.392893 292609 55 127734
SIFT-PCA 11 14281 67776 4.74588 6161.45 0.379411 295870 55 117257
DSP-SIFT 11 14867 71153 4.78596 6468.45 0.414944 293789 55 130820
ConvOpt 11 14717 70614 4.79812 6419.45 0.393435 296522 55 127540
TFeat 11 14273 67584 4.73509 6144.00 0.372782 298433 55 113928
LIFT 11 6003 28296 4.71364 2572.36 0.580594 304258 55 52293
Herzjesu SIFT 8 8 7502 31670 4.22154 3958.75 0.431632 241347 28 48965
SIFT-PCA 8 7161 29735 4.15235 3716.87 0.409061 245291 28 44443
DSP-SIFT 8 7769 32809 4.22306 4101.12 0.459535 238122 28 51893
ConvOpt 8 4957 20227 4.08049 2528.37 0.387640 242262 26 27830
TFeat 8 7061 29232 4.13992 3654.00 0.404879 247065 28 43297
LIFT 8 3742 14890 3.97915 1861.25 0.620034 241173 28 22683
South-Building SIFT 128 128 108124 653975 6.04838 5109.18 0.545747 2141964 3822 2036024
SIFT-PCA 128 105612 632145 5.98554 4938.63 0.531500 2090915 3979 1927873
DSP-SIFT 128 112719 666808 5.91566 5209.43 0.580537 2141873 3958 2076833
ConvOpt 128 62306 397579 6.38107 3106.08 0.487924 2117221 1901 984762
TFeat 128 102143 604357 5.91677 4721.53 0.510260 2089004 4342 1751327
LIFT 128 42601 233110 5.47193 1821.17 0.730874 2154755 2830 711142
Madrid Metropolis SIFT 1344 500 116088 733745 6.32053 1467.49 0.605330 1822434 227092 6969437
SIFT-PCA 469 111090 645437 5.81003 1376.19 0.586054 1571584 644573 13970478
DSP-SIFT 467 99514 649704 6.52877 1391.22 0.660135 1643614 135215 4586807
ConvOpt 348 40749 213176 5.23144 612.57 0.534638 1251705 665669 12531539
TFeat 435 102775 574980 5.59455 1321.79 0.566243 1536760 712501 15207011
LIFT 416 44056 303055 6.87885 728.497 0.768777 1577304 82562 2531640
Gendarmenmarkt SIFT 1463 1035 338972 1872308 5.52348 1809.00 0.699118 4225031 321854 12625310
SIFT-PCA 975 349217 1690464 4.84072 1733.80 0.701904 3649260 822997 20321433
DSP-SIFT 979 293209 1577921 5.38155 1611.76 0.749714 2600189 265575 9315075
ConvOpt 772 178859 694211 3.88133 899.23 0.723822 2955105 811724 15583270
TFeat 902 280233 1324931 4.72796 1468.88 0.695517 3384513 655181 15040928
LIFT 959 142982 819940 5.73456 854.99 0.841945 3939957 125084 5012767
Tower of London SIFT 1576 804 239951 1863301 7.76534 2317.53 0.615406 3050252 165097 11249925
SIFT-PCA 693 220381 1491686 6.76866 2152.50 0.602057 2518677 558173 14605601
DSP-SIFT 799 267906 1940752 7.24415 2428.97 0.655440 2946702 260963 12750104
ConvOpt 537 143397 788855 5.50119 1469.00 0.580207 2448215 742322 14648025
TFeat 675 255666 1605322 6.27898 2378.25 0.580068 2583560 926517 21742783
LIFT 713 96848 739340 7.63402 1036.94 0.728200 2879455 60841 3628677
Alamo SIFT 2915 963 198433 2437084 12.28164 2530.72 0.647271 3737516 64068 21263831
SIFT-PCA 921 197723 2279339 11.52791 2474.85 0.626812 3256364 143747 20145150
DSP-SIFT 961 223192 2564659 11.49082 2668.73 0.712005 3815012 79973 23375984
ConvOpt 684 110261 1167754 10.59081 1707.24 0.537849 2546861 168383 8065721
TFeat 865 180730 2040775 11.29184 2359.27 0.609598 2973035 192115 16518550
LIFT 796 78892 1011117 12.816471 1270.24 0.768177 2900266 40219 8151208
Roman Forum SIFT 2364 1679 433152 3603662 8.31962 2146.31 0.708420 9630170 76547 16424472
SIFT-PCA 1663 434317 3267075 7.52232 1964.56 0.674920 9379870 151694 15134227
DSP-SIFT 1644 464792 3653745 7.86103 2222.47 0.749306 9429283 100827 16469792
ConvOpt 1282 182922 1263324 6.90635 985.43 0.627904 7404163 158940 6151296
TFeat 1603 401965 2897537 7.20843 1807.57 0.647753 9096825 180301 12869235
LIFT 1503 174430 1420800 8.14538 945.30 0.814467 8584480 49413 5775222
Cornell SIFT 6514 6073 1847141 12865681 6.96518 2118.50 0.660522 35232209 227478 61428156
SIFT-PCA 6010 1856258 12307131 6.63007 2047.77 0.643796 35263104 417668 59874790
DSP-SIFT 6069 2071407 13671952 6.60032 2252.75 0.708143 35449395 283503 64364585
ConvOpt 5009 938316 6082683 6.48255 1214.35 0.570824 30619302 353461 25017605
TFeat 5779 1730263 11292717 6.52659 1954.09 0.622775 33917778 489447 55385797
LIFT 5518 739059 4602081 6.22694 834.01 0.730208 33372173 143408 19144270

Runtime:

Method Runtime Hardware
SIFT 9.3s (Intel E5-2697 2.60GHz CPU - single-threaded)
SIFT-PCA 10.5s (Intel E5-2697 2.60GHz CPU - single-threaded)
DSP-SIFT 23.7s (Intel E5-2697 2.60GHz CPU - single-threaded)
ConvOpt 49.9s (Intel E5-2697 2.60GHz CPU, NVIDIA Titan X GPU)
TFeat 11.8s (Intel E5-2697 2.60GHz CPU, NVIDIA Titan X GPU)
LIFT 212.3s (Intel E5-2697 2.60GHz CPU, NVIDIA Titan X GPU)

References:

  • SIFT: D.G. Lowe: Object Recognition from Local Scale-Invariant Features. ICCV, 1999. R. Arandjelovic and A. Zisserman. Three things everyone should know to improve object retrieval. CVPR, 2012.
  • SIFT-PCA: A. Bursuc, G. Tolias, and H. Jegou. Kernel local descriptors with implicit rotation matching. ACM Multimedia, 2015.
  • DSP-SIFT: J.Dong and S.Soatto. Domain-size pooling in local descriptors: DSP-SIFT. CVPR, 2015.
  • ConvOpt: K. Simonyan, A. Vedaldi, and A. Zisserman. Learning local feature descriptors using convex optimisation. PAMI, 2014.
  • TFeat: V.Balntas, E.Riba, D.Ponsa, and K.Mikolajczyk. Learning local feature descriptors with triplets and shallow convolutional neural networks. BMVC, 2016.
  • LIFT: M. Kwang, E. Trulls, V. Lepetit, and P. Fua. LIFT: Learned Invariant Feature Transform. ECCV, 2016.
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