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三次元形状とディープラーニング

 三次元形状とディープラーニング

三次元形状処理とディープラーニングの初歩についてまとめたスライドです。

Tatsuya Yatagawa

December 21, 2020
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  1. View Slide


  2. 機械学習
    ニューラルネット
    深層学習

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  3. • θ

    = ; = 2
    2 + 1
    + 0

    = arg min ෍
    =0
    −1
    (
    ,
    ; )

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  4. 0
    = 0
    + 0
    1
    = 1
    + 1
    2
    = 2
    + 2
    = 0
    0
    + 1
    1
    + 2
    2
    +
    = 0
    0
    + 1
    1
    + 2
    2

    +(0
    0
    + 1
    1
    + 2
    2
    + )

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  5. 0

    1

    2

    0
    = ReLU(0
    + 0
    )
    1
    = ReLU(1
    + 1
    )
    2
    = ReLU(2
    + 2
    )



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  6. = 32 = 128 = 512


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  7. = 512
    = 1 = = 4

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  15. (c) Velodyne
    (c) Andrew Tallon
    (c) Microsoft
    (c) Sony

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  16. , , = 0

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  17. ′ =


    ′ = +




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  18. , , = 0

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  19. , , = 0

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  30. , , = 0

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  31. (c) Velodyne

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  32. (0
    , 0
    , 0
    )
    (1
    , 1
    , 1
    )
    (
    ,
    ,
    )
    (−1
    , −1
    , −1
    )
    (+1
    , +1
    , +1
    )
    (0
    , 0
    , 0
    )
    (1
    , 1
    , 1
    )
    (−1
    , −1
    , −1
    )
    (+1
    , +1
    , +1
    )
    (
    ,
    ,
    )

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  43. , , = 0

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  46. ∗ = ℱ−1(ℱ ⋅ ℱ )
    ′ = ℱ−1(Θ ∘ ℱ )

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  47. =
    0 1 1 0
    1 0 1 0
    1 1 0 1
    0 0 1 0
    = − −
    1
    2−
    1
    2

    = ෍



    :

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  48. = T

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  49. • ()
    ′ = ℱ−1(Θ ∘ ℱ )
    ′ =

    ℱ = ()
    ℱ−1 = ()

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  50. +1

    +2


    +1 = ReLU ⋅ ෍



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  58. , , = 0

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  59. , , =

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