Deep 3D semantic scene extrapolation

Abbasi A., Kalkan S., Sahillioglu Y.

VISUAL COMPUTER, cilt.35, ss.271-279, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 35
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1007/s00371-018-1586-7
  • Dergi Adı: VISUAL COMPUTER
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.271-279
  • Anahtar Kelimeler: 3D scenes, Extrapolation, Convolutional neural networks, COMPLETION, OPTIMIZATION, REGISTRATION, GEOMETRY
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Scene extrapolation is a challenging variant of the scene completion problem, which pertains to predicting the missing part(s) of a scene. While the 3D scene completion algorithms in the literature try to fill the occluded part of a scene such as a chair behind a table, we focus on extrapolating the available half-scene information to a full one, a problem that, to our knowledge, has not been studied yet. Our approaches are based on convolutional neural networks (CNN). As input, we take the half of 3D voxelized scenes, then our models complete the other half of scenes as output. Our baseline CNN model consisting of convolutional and ReLU layers with multiple residual connections and Softmax classifier with voxel-wise cross-entropy loss function at the end. We train and evaluate our models on the synthetic 3D SUNCG dataset. We show that our trained networks can predict the other half of the scenes and complete the objects correctly with suitable lengths. With a discussion on the challenges, we propose scene extrapolation as a challenging test bed for future research in deep learning. We made our models available on https://github.com/aliabbasi/d3dsse.

Scene extrapolation is a challenging variant of the scene completion problem, which pertains to predicting the missing part(s) of a scene. While the 3D scene completion algorithms in the literature try to fill the occluded part of a scene such as a chair behind a table, we focus on extrapolating the available half-scene information to a full one, a problem that, to our knowledge, has not been studied yet. Our approaches are based on convolutional neural networks (CNN). As input, we take the half of 3D voxelized scenes, then our models complete the other half of scenes as output. Our baseline CNN model consisting of convolutional and ReLU layers with multiple residual connections and Softmax classifier with voxel-wise cross-entropy loss function at the end. We train and evaluate our models on the synthetic 3D SUNCG dataset. We show that our trained networks can predict the other half of the scenes and complete the objects correctly with suitable lengths. With a discussion on the challenges, we propose scene extrapolation as a challenging test bed for future research in deep learning. We made our models available on https://github.com/aliabbasi/d3dsse.