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NIPS2017reading_3Dreconstruction
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望月紅葉さんと幸せな家庭を築きたい
January 27, 2018
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NIPS2017reading_3Dreconstruction
望月紅葉さんと幸せな家庭を築きたい
January 27, 2018
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Transcript
̏࣍ݩ෮ݩʹؔͯ͠ Learning a Multi-View Stereo Machine NIPS2017จಡΈձˏΫοΫύου 1 ಛʹදه͕ͳ͍ݶΓɺҎԼͷࢿྉ͔ΒҾ༻ https://arxiv.org/pdf/1708.05375.pdf
Learning a Multi-View Stereo Machine ▸ චऀ • Abhishek Kar,
Christian Häne, Jitendra Malik ʢUC Berkeley) ▸ ֓ཁ • Multi View StereoʢMVSʣʹΑΔີͳ3࣍ݩ෮ݩΛDeep LearningͰEnd2Endʹֶश • MVSΛ”ֶशͰ͖Δ”ͷͰແ͍͔ͱ͍͏ٙʹ͑Δ 2
എܠ ▸ Multi View Stereoͱ 1. ಛநग़ 2. Ϛονϯά 3.
̏࣍ݩ෮ݩ 4. Τϥʔͷআڈ 3
എܠ ▸ Multi View Stereoͱ 1. ಛநग़ 2. Ϛονϯά 3.
̏࣍ݩ෮ݩ 4. Τϥʔͷআڈ ==> DeepԿͰશͯղܾͰ͖ͦ͏ 4
എܠ ▸ Multi View Stereoͱ 1. ಛநग़ɹ← CNNͰ͍͚Δ 2. Ϛονϯά
3. ̏࣍ݩ෮ݩ 4. Τϥʔͷআڈ 5
എܠ ▸ Multi View Stereoͱ 1. ಛநग़ 2. Ϛονϯάɹ← CNNͱRNNͰ͍͚Δ
3. ̏࣍ݩ෮ݩ 4. Τϥʔͷআڈ 6
എܠ ▸ Multi View Stereoͱ 1. ಛநग़ 2. Ϛονϯά 3.
̏࣍ݩ෮ݩɹ← DeconvͰ͍͚Δ 4. Τϥʔͷআڈ 7
എܠ ▸ Multi View Stereoͱ 1. ಛநग़ 2. Ϛονϯά 3.
̏࣍ݩ෮ݩ 4. Τϥʔͷআڈɹ← Encoder-DecoderͰ͍͚Δ 8
DeepԿͰࡾ࣍ݩ෮ݩ ▸ 3DR2N2(ECCV2016) • ෳը૾ΛΤϯίʔυ͠ɺLSTMͰϚονϯά 9 http://3d-r2n2.stanford.edu
DeepԿͰࡾ࣍ݩ෮ݩ ▸ 3D Shape Reconstruction by Modeling 2.5D Sketch (NIPS2017)
• ϦΞϧͷը૾͔Β2.5DͷεέονΛى͜͠ɺ2.5DεέονΛͱʹ 3DshapeਪఆΛEnd2EndֶशͰ͢Δ 10 https://arxiv.org/pdf/1711.03129.pdf
͢༰ ▸ શମ૾ ▸ ख๏ ▸ ࣮ݧ ▸ ·ͱΊ 11
શମ૾ 12 http://bair.berkeley.edu/blog/2017/09/05/unified-3d/
શମ૾ 13 Learnt Stereo Machines
ख๏ ▸ Image Encoder • Encoder-DecoderܕʢU-netʣͷઃܭ • Ϛονϯάʹ༻͍Δ̎DͷಛϚοϓ࡞ • ࣍ݩ2DnಛϚο
14
ख๏ ▸ Unplojection ▸ 2࣍ݩͷಛϚοϓ3࣍ݩͷຊདྷ͋Δ͖ಛϚοϓ͔ΒࣹӨ ▸ 3࣍ݩάϦουʹٯࣹӨ 15 http://bair.berkeley.edu/blog/2017/09/05/unified-3d/
ख๏ ▸ Unplojection ▸ 2࣍ݩͷಛϚοϓ3࣍ݩͷຊདྷ͋Δ͖ಛϚοϓ͔ΒࣹӨ ▸ 3࣍ݩάϦουʹٯࣹӨ 16 http://bair.berkeley.edu/blog/2017/09/05/unified-3d/
ख๏ ▸ Unplohection ▸ 2࣍ݩͷಛϚοϓ3࣍ݩͷຊདྷ͋Δ͖ಛϚοϓ͔ΒࣹӨ ▸ 3࣍ݩάϦουʹٯࣹӨ 17 http://bair.berkeley.edu/blog/2017/09/05/unified-3d/
ख๏ ▸ Unplohection ▸ 2࣍ݩͷಛϚοϓ3࣍ݩͷຊདྷ͋Δ͖ಛϚοϓ͔ΒࣹӨ ▸ 3࣍ݩάϦουʹٯࣹӨ 18 http://bair.berkeley.edu/blog/2017/09/05/unified-3d/
ख๏ ▸ Recurrent Grid Fusion • 3࣍ݩͷಛϚοϓͷϚονϯάΛGated Recurrent Unit(GRU)Ͱ •
GRUʹ͍࣋ͬͯͨ͘Ίɺ3D convolutionΛ༻ • ͜ͷաఔ͕MVSͷܭࢉϚονϯάΛ୲ • ֶशͷࡍը૾ͷೖྗॱΛϥϯμϜʹೖΕସ͑Δ 19
ख๏ ▸ 3D Grid Reasoning • GRUͰ̏࣍ݩάϦουʹͨ͠ΒϊΠζ͕ଟ͔ͬͨɻ • 3U-netͰEncode Decode͢ΔͱFilteringͰ͖Δ
20
ख๏ ▸ Differentiable Projection • Depthͷ෮ݩʹL1 loss(high frequency informationͷͨΊ) •
Voxelͷ෮ݩʹvoxel͝ͱͷcross entropy loss 21
࣮ݧ ▸ σʔληοτ • ShapeNetσʔλΛར༻ • ̏࣍ݩCADϞσϧͷެ։σʔληοτ 22 https://shapenet.cs.stanford.edu/shrec17/
࣮ݧ • ೖྗը૾ ▸ ShapeNetͷ3DϞσϧΛϨϯμϦϯάͯ͠224x224x3 ▸ ̍ࢹ͋ͨΓ̐ຕ ▸ Χϝϥϙʔζ •
Ξτϓοτ ▸ Depth: 224x224x3 ▸ Voxel: 32x32x32 23
࣮ݧ ▸ ݁Ռ 24 3DR2N2ͱൺɺࡉ͔͍෮ݩ͕Մೳ
࣮ݧ ▸ ݁Ռ 25 3DR2N2ͱൺɺগͳ͍ຕͰ෮ݩ͕Մೳ ຕ૿͑Δͱੑೳ্͕͕Δ
࣮ݧ ▸ ݁Ռ 26 stereo matchingͰ෮ݩ͠ͳ͍ ૭෮ݩՄೳ
࣮ݧ ▸ ݁Ռ 27 stereo matchingʹൺ গͳ͍ຕͰ෮ݩ͕Մೳ චऀᐌ͘ CNNͷίϯςΫετΛݟΔྗ ैདྷͷstereo
matchingΛ͙྇ DepthMapͷਪఆ݁ՌΛෳΈ߹Θͤͯ̏࣍ݩ෮ݩͨ͠
·ͱΊ ▸ Learnt Stereo MachinesΛఏҊ ▸ ෳࢹ͔Βͷೖྗը૾Λݩʹɺ DepthMapͱVoxelͷਪఆ͕Մೳͱͳͬͨ ▸ ՝
• ग़ྗVoxel͕32x32x32ͱখ͍͞ 28