Deep Learning on Meshes: Challenges and Applications

Exploring the use of deep learning on irregular mesh data and its applications in computer graphics

Slide1: Introduction to Deep Learning on Meshes

  • Deep learning has shown success on structured data like images and text
  • Interest is growing in applying deep learning to 3D computer graphics
  • Meshes are widely used in computer graphics due to their efficiency and flexibility
  • Deep learning on meshes opens doors for applications like generative shape modeling and shape analysis tasks

Slide2: Challenges in Extending Deep Learning to Meshes

  • Mesh representation lacks structure and order, making it challenging to apply standard neural network operators
  • Mesh triangulations can vary, making it difficult to utilize standard convolution operations
  • 3D mesh data is often imperfect and requires manual processing
  • The curse of dimensionality in 3D data restricts the amount of available training data

Slide3: Mesh Convolutional Neural Networks

  • Mesh CNNs learn deep features on mesh edges
  • Shared weights across the mesh enable efficient and effective feature extraction
  • Mesh pooling and unpooling layers increase the receptive field and incorporate more context
  • Different approaches like neural subdivision and mesh walker explore variations of mesh convolutions

Slide4: Learning from a Single Shape Example

  • Internal data within a single shape can be used for training deep neural networks
  • Self-prior allows learning from unique internal patch data within a shape
  • Applications include surface reconstruction from point clouds and shape completion
  • The self-prior provides a tailored prior for each individual input, resulting in better reconstructions

Slide5: Surface Reconstruction with Self-Prior

  • Surface reconstruction is traditionally a challenging and ill-posed problem
  • Point-to-mesh uses the self-prior to generate more plausible surface reconstructions
  • The self-prior is optimized through mesh convolutional neural networks
  • The self-prior enables better handling of noise, outliers, and missing parts

Slide6: Applications of Learning from Single Shape Example

  • Learning from a single shape example has various applications in computer graphics
  • Shape completion, 3D printing orientation, and internal structure learning are a few examples
  • By leveraging internal data, these applications can be more robust and tailored to specific shapes
  • The self-prior approach provides a powerful inductive bias for solving these tasks

Slide7: Conclusion

  • Deep learning on meshes opens up new possibilities in computer graphics
  • Challenges in mesh representation and data must be addressed
  • Mesh convolutional neural networks provide a powerful inductive bias for deep learning on meshes
  • Learning from a single shape example enables robust applications without the need for large external data sets

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