$30 off During Our Annual Pro Sale. View Details »

Unisim 超リアルな自動運転センサシミュレーション

abemii_
July 23, 2023

Unisim 超リアルな自動運転センサシミュレーション

第59回 コンピュータビジョン勉強会@関東「CVPR2023読み会(前編)」
https://kantocv.connpass.com/event/288899/

で発表した発表資料です。

UniSim: A Neural Closed-Loop Sensor Simulator

Ze Yang, Yun Chen, Jingkang Wang, Sivabalan Manivasagam,
Wei-Chiu Ma, Anqi Joyce Yang, Raquel Urtasun

abemii_

July 23, 2023
Tweet

More Decks by abemii_

Other Decks in Research

Transcript

  1. Unisim
    ௒ϦΞϧͳࣗಈӡస
    ηϯαγϛϡϨʔγϣϯ
    Michiya Abe
    @abemii_
    Jul. 23, 2023.
    ίϯϐϡʔλϏδϣϯษڧձ@ؔ౦
    CVPR 2023 ಡΈձʢલฤʣ

    View Slide

  2. 2
    l Michiya Abe
    l ܦྺ
    l 2019 ɿम࢜ʢ৘ใཧ޻ֶʣ
    l 2019 ~ ɿࣗಈӡస޲͚ը૾ೝࣝͷݚڀ։ൃ
    l ෺ମݕग़ɾ૸࿏ೝࣝͷϞσϧͷ։ൃ
    l ϞσϧͷྔࢠԽɼΤοδͰͷߴ଎Խ
    l ޷͖ͳ΋ͷ
    l ςΩετΤσΟλʢNeovimʣ
    l Ϊλʔʢॳ৺ऀʣ
    ࣗݾ঺հ
    Twitter: @abemii_
    Blog: https://abemii.hatenablog.com/
    ˞ ൃද಺༰͸ॴଐػؔͱҰ੾ؔ܎͠·ͤΜ

    View Slide

  3. 3
    l CVPR 2023 (Highlight)
    l ܭଌͨ͠ηϯασʔλʢը૾ɾLiDAR఺܈ʣ͔Βɼૢ࡞Մೳͳσδλϧπ
    ΠϯΛ࡞Δ
    l ৽͍͠γφϦΦΛ࡞Δ͜ͱ΋Ͱ͖ɼ closed-loop ධՁʹ΋࢖͑Δ
    ࠓ೔঺հ͢Δ࿦จ
    • Paper: https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_UniSim_A_Neural_Closed-Loop_Sensor_Simulator_CVPR_2023_paper.pdf
    • Project page: https://waabi.ai/unisim/

    View Slide

  4. ·ͣ͸σϞΛ͝ཡ͍ͩ͘͞

    View Slide

  5. 5
    Video: https://waabi.ai/unisim/
    σϞɿΞΫλʔʢଞं྆ʣͷআڈ
    Recorded Simulated
    Α͘ݟΔͱͪΐͬͱΞʔςΟϑΝΫτ͕ੜ͍ͯ͡Δ͕ɼӈ
    Ϩʔϯ͸ं͕྆ଟ͘ɼશ͘؍ଌͰ͖͍ͯͳ͍ྖҬ͕͋Δͷ
    ͩΖ͏

    View Slide

  6. 6
    ͜Μͳγʔϯ͸ك͕ͩɼ࣮֬ʹೝࣝͯࣗ͠ंͷߦಈΛม͑
    ͳ͍ͱࣄނʹͳͬͯ͠·͏
    σϞɿΞΫλʔΛૢ࡞
    Recorded Simulated
    Video: https://waabi.ai/unisim/

    View Slide

  7. 7
    σϞɿSDVʢࣗंʣͷηϯαҐஔΛૢ࡞ʢࠨӈɾ্Լʣ
    Video: https://waabi.ai/unisim/

    View Slide

  8. 8
    ࣗಈӡసͷ҆શੑͷݕূ = ೉͍͠
    l ΫϦςΟΧϧͳγʔϯΛݕূ͍ͨ͠
    → ͦΜͳγʔϯ͸࣮ੈքͰ͸໓ଟʹಘΒΕͳ͍
    l ࣮ੈքσʔλͷϩάϦϓϨΠ
    → ࣗंͷڍಈʹର͠ɼԠ౴తͰͳ͍
    → ࣗंͷߦಈʹର͢Δ݁Ռ͕Ͳ͏ͳΔ͔Θ͔Βͳ͍
    Ϟνϕʔγϣϯ

    View Slide

  9. 9
    Closed-loop Simulation
    l ͋ΔγʔϯͰࣗं͕͋ΔߦಈΛͨ͠ͱ͖ɼ
    पғ͸ͲͷΑ͏ʹมԽ͢Δ͔ʢͲͷΑ͏ͳ݁ՌΛ΋ͨΒ͔͢ʣ: “what-if”
    l ҆શͳࣗ཯૸ߦγεςϜͷධՁͷͨΊʹઈରʹඞཁ
    Ϟνϕʔγϣϯ
    ੈքͷঢ়ଶ
    ݱ࣮తͳηϯα
    γϛϡϨʔγϣϯ
    ԿΒ͔ͷ੍ޚ
    ଞͷΞΫλʔͷৼΔ෣͍ͷγϛϡϨʔγϣϯ + ੈքͷঢ়ଶͷߋ৽

    View Slide

  10. 10
    طଘͷ Closed-loop Simulation ͷ໰୊఺
    l εέʔϥϒϧͰͳ͍ɿਓ͕ؒγφϦΦΛख࡞Γɼγϯϓϧͳ੍ޚ
    l ଟ༷ੑʹ͚ܽΔɿγφϦΦ΍Ξηοτ͕ݶΒΕ͍ͯΔ
    l ࣮ࣸతͰͳ͍ɿೝࣝʹ͓͚ΔυϝΠϯΪϟοϓ͕ੜ͡Δ
    Ϟνϕʔγϣϯ
    ͜Ε͸
    Carla ͷྫ

    View Slide

  11. 11
    ຊݚڀ Unisim: εέʔϥϒϧɾଟ༷ɾ࣮ࣸత
    l ϦΞϧσʔλΛऩू͢Δ͚ͩͳͷͰɼεέʔϥϒϧ
    l ΞΫλʔΛૢΔ͜ͱ͕Ͱ͖ΔͷͰɼଟ༷ੑ΋͋Δ
    l ࣮ࣸతͳֆΛ࡞Δ͜ͱ͕Ͱ͖ΔʢϦΞϧͳηϯαγϛϡϨʔγϣϯʣ
    Ϟνϕʔγϣϯ
    UniSim
    ࣗंͷߦಈʹैͬͯɼଞͷ
    ΞΫλʔͷҐஔ΋มԽ͢Δ
    ˠมԽͨ͋͠ͱͷ؍ଌ΋γ
    ϛϡϨʔτͰ͖Δ

    View Slide

  12. 12
    ֓ཁ
    ಈతͳΞΫλʔ
    ੩తͳഎܠ
    εύʔεͳ
    άϦουͰ
    ϞσϧԽ
    ֶशՄೳͳજࡏද
    ݱ͔ΒϋΠύʔω
    οτϫʔΫͰදݱ
    Λੜ੒ NNF Λ߹੒
    ηϯαத৺͔ΒϨΠΛඈ͹͠ɼ
    ϘϦϡʔϜϨϯμϦϯάΛߦ͍ɼ
    ֤ηϯαʹσίʔυ
    ͦΕͧΕผʑʹଟॏղ૾౓ಛ௃άϦου
    ͰϞσϧԽ
    3࣍ݩͷγʔϯΛ
    • ੩తͳഎܠͱ
    • ಈతͳΞΫλʔͷू߹
    ʹ෼཭
    ਤ͸ [1] ಈըͷ34:00 ͔ΒҾ༻ͯ͠Ճ޻

    View Slide

  13. 13
    ֓ཁ
    ಈతͳΞΫλʔ
    ੩తͳഎܠ
    εύʔεͳ
    άϦουͰ
    ϞσϧԽ
    ֶशՄೳͳજࡏද
    ݱ͔ΒϋΠύʔω
    οτϫʔΫͰදݱ
    Λੜ੒ NNF Λ߹੒
    ηϯαத৺͔ΒϨΠΛඈ͹͠ɼ
    ϘϦϡʔϜϨϯμϦϯάΛߦ͍ɼ
    ֤ηϯαʹσίʔυ
    ͦΕͧΕผʑʹଟॏղ૾౓ಛ௃άϦου
    ͰϞσϧԽ
    3࣍ݩͷγʔϯΛ
    • ੩తͳഎܠͱ
    • ಈతͳΞΫλʔͷू߹
    ʹ෼཭
    ਤ͸ [1] ಈըͷ34:00 ͔ΒҾ༻ͯ͠Ճ޻

    View Slide

  14. 14
    ֓ཁ
    ಈతͳΞΫλʔ
    ੩తͳഎܠ
    εύʔεͳ
    άϦουͰ
    ϞσϧԽ
    ֶशՄೳͳજࡏද
    ݱ͔ΒϋΠύʔω
    οτϫʔΫͰදݱ
    Λੜ੒ NNF Λ߹੒
    ηϯαத৺͔ΒϨΠΛඈ͹͠ɼ
    ϘϦϡʔϜϨϯμϦϯάΛߦ͍ɼ
    ֤ηϯαʹσίʔυ
    ͦΕͧΕผʑʹଟॏղ૾౓ಛ௃άϦου
    ͰϞσϧԽ
    3࣍ݩͷγʔϯΛ
    • ੩తͳഎܠͱ
    • ಈతͳΞΫλʔͷू߹
    ʹ෼཭
    ਤ͸ [1] ಈըͷ34:00 ͔ΒҾ༻ͯ͠Ճ޻

    View Slide

  15. 15
    ֓ཁ
    ಈతͳΞΫλʔ
    ੩తͳഎܠ
    εύʔεͳ
    άϦουͰ
    ϞσϧԽ
    ֶशՄೳͳજࡏද
    ݱ͔ΒϋΠύʔω
    οτϫʔΫͰදݱ
    Λੜ੒ NNF Λ߹੒
    ηϯαத৺͔ΒϨΠΛඈ͹͠ɼ
    ϘϦϡʔϜϨϯμϦϯάΛߦ͍ɼ
    ֤ηϯαʹσίʔυ
    ͦΕͧΕผʑʹଟॏղ૾౓ಛ௃άϦου
    ͰϞσϧԽ
    3࣍ݩͷγʔϯΛ
    • ੩తͳഎܠͱ
    • ಈతͳΞΫλʔͷू߹
    ʹ෼཭
    ਤ͸ [1] ಈըͷ34:00 ͔ΒҾ༻ͯ͠Ճ޻

    View Slide

  16. ख๏

    View Slide

  17. 17
    γʔϯΛ Neural Network Ͱදݱ
    l NFF ͸ NeRF ΍ Occupancy ؔ਺ͷ্Ґू߹
    l ߹੒Մೳ
    = ͍͔ͭ͘ͷγϯϓϧͳ NFF Λ૊Έ߹Θͤͯɼෳࡶͳ৔Λ࡞Δ͜ͱ͕Ͱ͖Δ
    Neural Feature Field (NFF)
    NeRF ͳΒ…
    • s: ϘϦϡʔϜີ౓
    • f: RGB radiance
    Occupancy Func. ͳΒ…
    • s: occupancy ͷ֬཰
    3࣍ݩ্
    ͷҐஔ x
    ࢹ఺
    ํ޲ d
    ҉໧తͳ
    δΦϝτϦ s
    ಛ௃
    هड़ࢠ f

    View Slide

  18. 18
    ֶशՄೳͳଟॏղ૾౓ಛ௃άϦουΛ
    NNFͱ૊Έ߹ΘͤΔ
    େҬಛ௃ͱہॴಛ௃Λཱ྆ͨ͠ޮ཰తͳಛ௃άϦου
    ΫΤϦϙΠϯτ
    ଟॏղ૾౓ಛ௃άϦου
    ֤ϨϕϧͰ
    τϦϦχΞิؒ
    ࢹ఺ϕΫτϧͱ݁߹ͯ͠ɼ
    .-1IFBEͰॲཧ
    MLP
    ジオ
    メト

    特徴
    記述

    l େҬతͳίϯςΫετͱɼࡉ͔͍ಛ௃ͷ྆ํΛΤϯίʔυͰ͖Δ
    l MLP ϔουͷେ͖͞Λখ͘͞Ͱ͖ɼਪ࿦࣌ؒΛݮΒͤΔ
    l ࣮ࡍʹ͸ɼ Instant-NGP [2] ͷΑ͏ͳɼଟॏղ૾౓ϋογϡΤϯίʔσΟϯάͰ࣮
    ૷͞Ε͍ͯΔΒ͍͠ʢ͋·Γࡉ͔͍৘ใ͸ॻ͍͍ͯͳ͍ʣ
    [2] Thomas Muller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. In SIGGRAPH, 2022.

    View Slide

  19. 19
    ܭଌं͕྆௨ա͖ͯͨ͠ྖҬΛ੩తͳഎܠͱಈతͳ
    ΞΫλʔʹ෼͚ͯϞσϧԽ͢Δ
    l ผʑͰ 3D ۭؒϘϦϡʔϜΛఆٛ
    l ੩తͳഎܠɿੈք࠲ඪܥͰදݱ
    l ಈతͳΞΫλʔɿͦΕͧΕͷ෺ମத৺࠲ඪܥͰදݱ
    l ͦΕͧΕผʑͷଟॏղ૾౓ಛ௃άϦουͱ
    NFF ͰϞσϧԽ͢Δ
    l ֤ΞΫλʔͷ 3D ϞʔγϣϯΛ੾Γ཭͠ɼ
    ܗঢ়ͱ֎؍ͷදݱʹूதͰ͖Δ
    l ܗঢ়ͷදݱʹ͸ɼූ߸෇͖ڑ཭ؔ਺ (SDF) Λ࢖͏ɽ
    ߹੒χϡʔϥϧදݱ
    Dynamic Actors
    Static Background B
    ݁ߏͳ͕͍ڑ཭૸ߦ
    → ޮ཰తͳදݱ͕ඞཁ
    SDV
    A1
    A2
    ɿA1
    ͷي੻
    ɿA2
    ͷي੻

    View Slide

  20. 20
    l ΄ͱΜͲϑϦʔεϖʔεͳͷͰɼಛ௃άϦουΛૄʹͰ͖ɼܭࢉίετΛݮΒͤΔ
    l Geometric Prior Λ༻͍Δ͜ͱͰɼγʔϯͷ 3D ߏ଄ΛΑΓద੾ʹϞσϧԽͰ͖Δ
    l େ͖ͳ֎ૠΛ൐͏৽͍͠ࢹ఺ΛγϛϡϨʔτ͢Δͱ͖ʹ΋ޮՌత
    ີͳߴղ૾౓ϘΫηϧάϦου͸อ࣋Ͱ͖ͳ͍
    → ද໘ۙ๣ͷϘΫηϧͷಛ௃͚ͩΛ࠷దԽ
    ૄͳഎܠγʔϯϞσϧ
    t = 1
    t = 4
    t = 7
    ఺܈ͷਤ: https://scale.com/open-av-datasets/pandaset ͔ΒҾ༻
    ֤ϑϨʔϜ͔Β LiDAR ఺܈Λू໿͠ɼ
    ີͳ 3D γʔϯ఺܈Λͭ͘Δ
    ू໿
    ͨ࢟͠
    Voxelize Morphology
    Dilation
    & ෼ׂ
    ϑϦʔεϖʔε
    ີͳ Occ. Grid
    ද໘ۙ๣ۭؒ
    փ৭ͷ෦෼͕࡞ΒΕΔ

    View Slide

  21. 21
    l ͦΕͧΕͷΞΫλʔͷಛ௃άϦουΛͲ͏΍ͬͯಘΕ͹ྑ͍ʁ
    l Կ͕خ͍͠ʁ
    l ҟͳΔΞΫλʔ͸ҟͳΔࢹ఺͔Β؍ଌ͞ΕΔ
    → ಛ௃άϦου͕ҟͳΔྖҬͰ΋༗ӹ
    l ࣄલ෼෍ֶ͕श͞ΕΔ͜ͱͰɼಛ௃ؒͷ૬͕ؔัଊ͞Εɼ
    ݟ͑Δ෦෼͔Βݟ͑ͳ͍෦෼͕ਪଌͰ͖Δ
    ҰൠԽΞΫλʔϞσϧ
    ֤ΞΫλʔΛผʑͷಛ௃άϦουͰ
    ύϥϝʔλԽ
    😖 ϝϞϦ͕ͨ͘͞Μඞཁ
    😖 Overfitɿະ஌ͷࢹ఺ʹ൚Խ͠ͳ͍
    ֤ΞΫλʔΛ௿࣍ݩͷજࡏίʔυͰ
    ϞσϧԽ͠ɼϋΠύʔωοτϫʔΫ
    ʹΑΓಛ௃άϦουΛճؼ
    ಈతͳ
    ΞΫλʔ

    View Slide

  22. 22
    ֤ΞΫλʔͷNFFΛੈք࠲ඪʹม׵ͯ͠
    എܠͱஔ͖׵͑Δ
    l ֤ΞΫλʔΛॴ๬ͷϙʔζͰม׵
    l ੩తͳഎܠ͸ૄͳಛ௃άϦουͳͷͰɼ୯ʹϑϦʔεϖʔεͱஔ͖׵͑Δ
    l ͜ΕʹΑΓɼγʔϯͷ࠶ߏ੒ɼΞΫλʔͷૠೖɾ࡟আɾૢ࡞ɼࣗंͷૢ࡞
    ͕Ͱ͖ΔΑ͏ʹͳΔ
    NFF ͷ߹੒
    SDV
    A1
    SDV
    A1
    A2
    SDV
    ΦϦδφϧ ૠೖ ࡟আ
    SDV
    A1
    ૢ࡞

    View Slide

  23. 23
    l Χϝϥը૾ɿ2D ͷҐஔʢϐΫηϧʣ + ஋ʢRGBʣ
    l Hybrid volume and neural rendering
    ϚϧνϞʔμϧͳηϯαγϛϡϨʔγϣϯʢΧϝϥը૾ + LiDAR ఺܈ʣ
    視点
    多重解像度特徴
    グリッド
    MLP
    実際の画像より⼩さい = 効率的
    ηϯαத৺ o
    Ray
    ํ޲ d
    2D ͷಛ௃Ϛοϓ F ͔Βɼ
    2D ͷ CNN Λ࢖ͬͯɼ
    RGB ը૾ΛϨϯμϦϯά
    αϯϓϧΛू໿͠ɼϘϦϡʔϜϨϯμ
    ϦϯάʹΑΓ 2D ͷϐΫηϧ͝ͱͷ
    ಛ௃هड़ࢠΛಘΔ
    Ray ʹԊͬͨ 3D ͷϙΠϯτΛ
    நग़͠ɼNNF ʹΑΓಛ௃ f ͱ
    δΦϝτϦ s ΛಘΔ
    Opacity (不透明度)
    SDF s の関数となっている
    近似的なステップ関数
    この重みは物体の表⾯付
    近に集中するような分布
    になっているはず

    View Slide

  24. 24
    l LiDAR ఺܈ɿ3DͷҐஔʢਂ౓ʣ+ ڧ౓ʢ൓ࣹ཰ʣ
    ϚϧνϞʔμϧͳηϯαγϛϡϨʔγϣϯʢΧϝϥը૾ + LiDAR ఺܈ʣ
    ਂ౓
    αϯϓϧ͞Εͨ 3D ఺ͷਂ౓ͷظ଴஋
    Λܭࢉ͠ɼਂ౓ͷܭଌΛγϛϡϨʔτ
    ͢Δ
    MLP
    視点
    多重解像度特徴
    グリッド
    MLP
    ηϯαத৺ o
    Ray
    ํ޲ d
    Ray ʹԊͬͨ 3D ͷϙΠϯτΛ
    நग़͠ɼNNF ʹΑΓಛ௃ f ͱ
    δΦϝτϦ s ΛಘΔ
    ڧ౓
    ϘϦϡʔϜϨϯμϦϯάͨ݁͠ՌΛ
    MLPͷσίʔμͰॲཧͯ͠༧ଌ
    (同じ)

    View Slide

  25. 25
    l ࠷దԽର৅ɿ͢΂ͯͷಛ௃άϦου
    ʢʴજࡏίʔυɼϋΠύʔωοτϫʔΫɼMLPϔουɼσίʔμʔʣ
    l ଛࣦؔ਺
    ֶशํ๏
    Χϝϥը૾
    ͷ࠶ߏ੒
    L2 photometric loss
    Perceptual loss
    ʢֶशࡁΈ VGG ͷग़ྗΛൺֱʣ
    LiDAR ఺܈
    ͷ࠶ߏ੒
    ఺܈͸ϊΠδʔͳͷͰɼਂ౓ͷޡ͕ࠩେ͖͍ 5 % Λ֎Ε஋ͱͯ͠আڈ͢Δ
    ਖ਼ଇԽ
    • ϘϦϡʔϜϨϯμϦϯάͷαϯϓϧͷॏΈ෼෍ w ͕ද໘पลʹूத͢ΔΑ͏ʹ
    • SDF s ͕ Eikonal ํఔࣜΛຬͨ͢Α͏ʹ ʢSmooth zero level set ʣ
    ఢରతଛࣦ
    • ؍ଌࢹ఺ͱະ؍ଌࢹ఺ͰͷγϛϡϨʔγϣϯը૾Λ۠ผͰ͖ͳ͘͢ΔΑ͏ʹ
    ʢະ؍ଌࢹ఺Ͱͷੜ੒ͷ඼࣭Λ্͍͛ͨʣ
    外挿設定で
    効果あり!

    View Slide

  26. 26
    l ࠷దԽର৅ɿ͢΂ͯͷಛ௃άϦου
    ʢʴજࡏίʔυɼϋΠύʔωοτϫʔΫɼMLPϔουɼσίʔμʔʣ
    l ଛࣦؔ਺
    ֶशํ๏
    Χϝϥը૾
    ͷ࠶ߏ੒
    L2 photometric loss
    Perceptual loss
    ʢֶशࡁΈ VGG ͷग़ྗΛൺֱʣ
    LiDAR ఺܈
    ͷ࠶ߏ੒
    ఺܈͸ϊΠδʔͳͷͰɼਂ౓ͷޡ͕ࠩେ͖͍ 5 % Λ֎Ε஋ͱͯ͠আڈ͢Δ
    ਖ਼ଇԽ
    • ϘϦϡʔϜϨϯμϦϯάͷαϯϓϧͷॏΈ෼෍ w ͕ද໘पลʹूத͢ΔΑ͏ʹ
    • SDF s ͕ Eikonal ํఔࣜΛຬͨ͢Α͏ʹ ʢSmooth zero level set ʣ
    ఢରతଛࣦ
    • ؍ଌࢹ఺ͱະ؍ଌࢹ఺ͰͷγϛϡϨʔγϣϯը૾Λ۠ผͰ͖ͳ͘͢ΔΑ͏ʹ
    ʢະ؍ଌࢹ఺Ͱͷੜ੒ͷ඼࣭Λ্͍͛ͨʣ
    外挿設定で
    効果あり!

    View Slide

  27. 27
    l ࠷దԽର৅ɿ͢΂ͯͷಛ௃άϦου
    ʢʴજࡏίʔυɼϋΠύʔωοτϫʔΫɼMLPϔουɼσίʔμʔʣ
    l ଛࣦؔ਺
    ֶशํ๏
    Χϝϥը૾
    ͷ࠶ߏ੒
    L2 photometric loss
    Perceptual loss
    ʢֶशࡁΈ VGG ͷग़ྗΛൺֱʣ
    LiDAR ఺܈
    ͷ࠶ߏ੒
    ఺܈͸ϊΠδʔͳͷͰɼਂ౓ͷޡ͕ࠩେ͖͍ 5 % Λ֎Ε஋ͱͯ͠আڈ͢Δ
    ਖ਼ଇԽ
    • ϘϦϡʔϜϨϯμϦϯάͷαϯϓϧͷॏΈ෼෍ w ͕ද໘पลʹूத͢ΔΑ͏ʹ
    • SDF s ͕ Eikonal ํఔࣜΛຬͨ͢Α͏ʹ ʢSmooth zero level set ʣ
    ఢରతଛࣦ
    • ؍ଌࢹ఺ͱະ؍ଌࢹ఺ͰͷγϛϡϨʔγϣϯը૾Λ۠ผͰ͖ͳ͘͢ΔΑ͏ʹ
    ʢະ؍ଌࢹ఺Ͱͷੜ੒ͷ඼࣭Λ্͍͛ͨʣ
    外挿設定で
    効果あり!

    View Slide

  28. 28
    l ࠷దԽର৅ɿ͢΂ͯͷಛ௃άϦου
    ʢʴજࡏίʔυɼϋΠύʔωοτϫʔΫɼMLPϔουɼσίʔμʔʣ
    l ଛࣦؔ਺
    ֶशํ๏
    Χϝϥը૾
    ͷ࠶ߏ੒
    L2 photometric loss
    Perceptual loss
    ʢֶशࡁΈ VGG ͷग़ྗΛൺֱʣ
    LiDAR ఺܈
    ͷ࠶ߏ੒
    ఺܈͸ϊΠδʔͳͷͰɼਂ౓ͷޡ͕ࠩେ͖͍ 5 % Λ֎Ε஋ͱͯ͠আڈ͢Δ
    ਖ਼ଇԽ
    • ϘϦϡʔϜϨϯμϦϯάͷαϯϓϧͷॏΈ෼෍ w ͕ද໘पลʹूத͢ΔΑ͏ʹ
    • SDF s ͕ Eikonal ํఔࣜΛຬͨ͢Α͏ʹ ʢSmooth zero level set ʣ
    ఢରతଛࣦ
    • ؍ଌࢹ఺ͱະ؍ଌࢹ఺ͰͷγϛϡϨʔγϣϯը૾Λ۠ผͰ͖ͳ͘͢ΔΑ͏ʹ
    ʢະ؍ଌࢹ఺Ͱͷੜ੒ͷ඼࣭Λ্͍͛ͨʣ
    外挿設定で
    効果あり!

    View Slide

  29. ࣮ݧ

    View Slide

  30. 30
    طଘख๏Λ྇կ
    ࣮ݧɿ৽͍͠Ϗϡʔͷੜ੒
    ఏҊख๏
    • Interpolation: 1 ϑϨʔϜ͓͖ʹαϯϓϧֶͯ͠शɼ࢒ΓͰݕূ
    • Lane shift: ԣʹ 2 ~ 3 m ఔ౓ͣΕͨࢹ఺͔Βੜ੒

    View Slide

  31. 31
    طଘख๏Λ྇կ
    ࣮ݧɿ৽͍͠Ϗϡʔͷੜ੒
    • Interpolation: 1 ϑϨʔϜ͓͖ʹαϯϓϧֶͯ͠शɼ࢒ΓͰݕূ
    • Lane shift: ԣʹ 2 ~ 3 m ఔ౓ͣΕͨࢹ఺͔Βੜ੒
    • ं྆ͷੜ੒඼࣭
    • ϨʔϯϥΠϯ΍࿏໘ඪࣔͷੜ੒඼࣭
    • ੜ੒͞ΕΔը૾ͷߴਫ਼ࡉ͞
    • FVS [6] ͸ͲͪΒ΋ΘΓͱྑͦ͞͏ʁ
    [6] Gernot Riegler and Vladlen Koltun. Free view synthesis. In ECCV, 2020.

    View Slide

  32. 32
    طଘख๏Λ྇կ
    ࣮ݧɿ৽͍͠Ϗϡʔͷੜ੒
    ఏҊख๏
    • Interpolation: 1 ϑϨʔϜ͓͖ʹαϯϓϧֶͯ͠शɼ࢒ΓͰݕূ
    • Lane shift: ԣʹ 2 ~ 3 m ఔ౓ͣΕͨࢹ఺͔Βੜ੒
    • ະ஌ࢹ఺ͩͱɼഎܠͷੜ੒඼࣭ʹ͔ͳΓͷ͕ࠩ͋Γͦ͏
    • طଘख๏͸΄΅ੜ੒Ͱ͖͍ͯͳ͍

    View Slide

  33. 33
    طଘख๏Λ྇կ
    ࣮ݧɿ৽͍͠Ϗϡʔͷੜ੒
    • ผͷྫͰ΋ಉ༷ͷ܏޲͕ΈΒΕΔ
    • Interpolation: 1 ϑϨʔϜ͓͖ʹαϯϓϧֶͯ͠शɼ࢒ΓͰݕূ
    • Lane shift: ԣʹ 2 ~ 3 m ఔ౓ͣΕͨࢹ఺͔Βੜ੒

    View Slide

  34. 34
    ఆྔతʹ΋طଘख๏ΑΓ΋ྑ͍
    ࣮ݧɿ ৽͍͠Ϗϡʔͷੜ੒
    • Interpolation: 1 フレームおきにサンプルして学習,残りで検証
    • Lane shift: 横に 2 ~ 3 m 程度ずれた視点から⽣成

    View Slide

  35. 35
    ϊΠζ͕গͳ͘ɼΑΓ࿈ଓతͳϏʔϜϦϯάΛ΋ͭ
    LiDAR ఺܈͕ੜ੒
    ࣮ݧɿLiDAR఺܈ͷ߹੒

    View Slide

  36. 36
    Perception ͷධՁ΍ֶशʹ࢖͑Δ͔ʁ
    Domain gap は少ない?
    ϦΞϧ
    σʔλ
    ֶश ධՁ
    ߹੒
    σʔλ
    ߹੒
    σʔλ
    ֶश ධՁ
    ϦΞϧ
    σʔλ
    ͜ͷ͕ࠩখ͍͞ͱ͍͍ͳ
    • -PH3FQMBZ -BOF4IJGUͷ྆ઃఆͰ΄΅ࠩͳ͠
    • 3FBMˠ3FBM͔ΒͷੑೳྼԽ΋খ͍͞
    Data Augmentation としても使える?
    Real → Real は 40.9%
    Real → Real は 40.9%
    ߹੒σʔλ͚ͩΛֶशʹ࢖͏৔߹
    • طଘख๏͸ੑೳྼԽ͕େ͖͍͕ɼఏҊख๏Ͱ͸
    Ή͠Ζੑೳ͕ྑ͘ͳͬͨ
    ϦΞϧ ߹੒σʔλͷ৔߹
    • ఏҊख๏͸ੑೳ޲্͕େ͖͍
    → Domain gap は⼗分⼩さい ˠ %BUB"VHNFOUBUJPOͱͯ͠΋༗ޮ

    View Slide

  37. 37
    Perception ͷධՁ΍ֶशʹ࢖͑Δ͔ʁ
    Domain gap は少ない?
    ϦΞϧ
    σʔλ
    ֶश ධՁ
    ߹੒
    σʔλ
    ߹੒
    σʔλ
    ֶश ධՁ
    ϦΞϧ
    σʔλ
    ͜ͷ͕ࠩখ͍͞ͱ͍͍ͳ
    • -PH3FQMBZ -BOF4IJGUͷ྆ઃఆͰ΄΅ࠩͳ͠
    • 3FBMˠ3FBM͔ΒͷੑೳྼԽ΋খ͍͞
    Data Augmentation としても使える?
    Real → Real は 40.9%
    Real → Real は 40.9%
    ߹੒σʔλ͚ͩΛֶशʹ࢖͏৔߹
    • طଘख๏͸ੑೳྼԽ͕େ͖͍͕ɼఏҊख๏Ͱ͸
    Ή͠Ζੑೳ͕ྑ͘ͳͬͨ
    ϦΞϧ ߹੒σʔλͷ৔߹
    • ఏҊख๏͸ੑೳ޲্͕େ͖͍
    → Domain gap は⼗分⼩さい ˠ %BUB"VHNFOUBUJPOͱͯ͠΋༗ޮ

    View Slide

  38. 38
    Perception ͷධՁ΍ֶशʹ࢖͑Δ͔ʁ
    Domain gap は少ない?
    ϦΞϧ
    σʔλ
    ֶश ධՁ
    ߹੒
    σʔλ
    ߹੒
    σʔλ
    ֶश ධՁ
    ϦΞϧ
    σʔλ
    ͜ͷ͕ࠩখ͍͞ͱ͍͍ͳ
    • -PH3FQMBZ -BOF4IJGUͷ྆ઃఆͰ΄΅ࠩͳ͠
    • 3FBMˠ3FBM͔ΒͷੑೳྼԽ΋খ͍͞
    Data Augmentation としても使える?
    Real → Real は 40.9%
    Real → Real は 40.9%
    ߹੒σʔλ͚ͩΛֶशʹ࢖͏৔߹
    • طଘख๏͸ੑೳྼԽ͕େ͖͍͕ɼఏҊख๏Ͱ͸
    Ή͠Ζੑೳ͕ྑ͘ͳͬͨ
    ϦΞϧ ߹੒σʔλͷ৔߹
    • ఏҊख๏͸ੑೳ޲্͕େ͖͍
    → Domain gap は⼗分⼩さい ˠ %BUB"VHNFOUBUJPOͱͯ͠΋༗ޮ

    View Slide

  39. ·ͱΊ

    View Slide

  40. 40
    l ࣮ੈքͷγφϦΦΛ׆༻ͯ͠ɼࣗ཯γεςϜͷςετʹ΋࢖༻Ͱ͖Δຊ෺
    ͦͬ͘ΓͷԾ૝ੈքΛߏங
    l ΧϝϥɾLiDAR ͷγʔέϯεΛೖྗͱ͠ɼಈతͳΞΫλʔͱ੩తͳഎܠΛ
    ෼ղɾ࠶ߏ੒Ͱ͖ΔχϡʔϥϧηϯαγϛϡϨʔλ
    l υϝΠϯΪϟοϓ͕খ͘͞ɼੜ੒͞Εͨ৽͍͠γφϦΦʹΑΔ Closed-
    loop ςετʹ΋࢖༻Մೳ
    l ࠓޙͷ՝୊ɾݶք
    l র໌৚݅ɾఱީ৚݅΁ͷରԠ
    l ଟؔઅΞΫλʔ΁ͷରԠ
    ·ͱΊ
    [4] Jingkang Wang, Siva Manivasagam, Yun Chen, Ze Yang , Ioan Andrei Bârsan, Anqi Joyce Yang, Wei–Chiu Ma, Raquel Urtasun. CADSim: Robust and Scalable in-the-wild 3D Reconstruction for
    Controllable Sensor Simulation. In CoRL, 2022.
    ଟؔઅΞΫλʔͷྫ [4] ΑΓҾ༻

    View Slide

  41. 41
    1. Raquel Urtasun. Next Generation Simulation for the Safe Development and Deployment of Self-Driving Technology. In CVPR Vision-
    Centric Autonomous Driving workshop, 2023. https://www.youtube.com/watch?v=0RjF9xbkiAY
    2. Thomas Muller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash
    encoding. In SIGGRAPH, 2022.
    3. Pengchuan Xiao, Zhenlei Shao, Steven Hao, Zishuo Zhang, Xiaolin Chai, Judy Jiao, Zesong Li, Jian Wu, Kai Sun, Kun Jiang, et al.
    Pandaset: Advanced sensor suite dataset for autonomous driving. In ITSC, 2021 https://scale.com/open-av-datasets/pandaset
    4. Jingkang Wang, Siva Manivasagam, Yun Chen, Ze Yang , Ioan Andrei Bârsan, Anqi Joyce Yang, Wei–Chiu Ma, Raquel Urtasun.
    CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation. In CoRL, 2022.
    5. Julian Ost, Fahim Mannan, Nils Thuerey, Julian Knodt, and Felix Heide. Neural scene graphs for dynamic scenes. In CVPR, 2021.
    6. Gernot Riegler and Vladlen Koltun. Free view synthesis. In ECCV, 2020.
    ࢀߟจݙ

    View Slide