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SurfNet: Generating 3D shape surfaces using deep residual networks (1703.04079v1)

Published 12 Mar 2017 in cs.CV and cs.CG

Abstract: 3D shape models are naturally parameterized using vertices and faces, \ie, composed of polygons forming a surface. However, current 3D learning paradigms for predictive and generative tasks using convolutional neural networks focus on a voxelized representation of the object. Lifting convolution operators from the traditional 2D to 3D results in high computational overhead with little additional benefit as most of the geometry information is contained on the surface boundary. Here we study the problem of directly generating the 3D shape surface of rigid and non-rigid shapes using deep convolutional neural networks. We develop a procedure to create consistent `geometry images' representing the shape surface of a category of 3D objects. We then use this consistent representation for category-specific shape surface generation from a parametric representation or an image by developing novel extensions of deep residual networks for the task of geometry image generation. Our experiments indicate that our network learns a meaningful representation of shape surfaces allowing it to interpolate between shape orientations and poses, invent new shape surfaces and reconstruct 3D shape surfaces from previously unseen images.

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Authors (4)
  1. Ayan Sinha (8 papers)
  2. Asim Unmesh (3 papers)
  3. Qixing Huang (78 papers)
  4. Karthik Ramani (23 papers)
Citations (175)

Summary

  • The paper introduces a novel method using geometry images to efficiently capture 3D shape surfaces by reducing volumetric overhead.
  • The study extends deep residual networks to separately learn x, y, and z coordinate maps, capturing complex surface details and high-frequency features.
  • The paper demonstrates successful 3D reconstruction from images and parametric data, offering promising applications in virtual reality and computer-aided design.

Analysis of "SurfNet: Generating 3D Shape Surfaces Using Deep Residual Networks"

The paper, "SurfNet: Generating 3D Shape Surfaces Using Deep Residual Networks," offers an important advancement in the field of 3D shape surface generation. The authors propose a methodology that leverages deep residual networks to generate 3D surfaces directly from parameteric representations or image data, a step forward in overcoming the computational difficulties associated with traditional voxel-based 3D representations.

The paper is premised on the observation that critical geometrical information in 3D shapes resides predominantly on the surfaces, suggesting that voxel representations, which include volumetric data, largely introduce unnecessary computational overhead. In response, the authors present a technique called 'geometry images', which captures 3D shape surfaces as consistent 2D parameterizations, reducing the complexity of 3D convolutions.

Key Contributions:

  1. Geometry Image Creation: The paper introduces a robust process for generating 'geometry images' for genus-0 shapes, ensuring consistency across a shape category. This method resolves common issues related to variability in parameterizations and discrepancies in capturing surface features across different shapes.
  2. Network Architecture: The authors extend deep residual networks to generate geometry images, showing the network's capability to capture complex surface geometries including high-frequency details and implicit pose estimates. Separate networks learn different coordinate geometry images (x,y,zx, y, z), enhancing fidelity over single network approaches.
  3. Shape Generation from Images and Parametric Representations: By developing tailored network architectures, the paper demonstrates 3D shape surface reconstruction from single RGB or depth images and generative modeling from parametric input vectors. The results showcase the model's capability in shape interpolation and morphing between different poses and surfaces, illustrating the network's learnings beyond simple memorization of training data.

Numerical Results:

The experiments conducted reveal strong performance in various tasks, including the reconstruction of non-rigid hand models with high articulation accuracy from depth images, and generation of rigid shapes (e.g., cars and airplanes) with proper viewpoint estimations from RGB inputs. The shape-aware loss function further enhances performance by preserving sharp edges, as evidenced by quantitative assessments in the paper.

Implications and Future Directions:

The method proposed provides promising implications for applications in virtual reality, computer-aided design, and 3D content creation, owing to its efficiency in generating high-quality 3D surfaces. Theoretically, it illustrates a potent pathway towards reducing complexity in 3D shape generation while maintaining shape fidelity and detail.

Future endeavors may improve the capabilities of SurfNet by expanding the framework to handle complex topologies beyond genus-0 surfaces and integrate more comprehensive training datasets, potentially optimizing correspondence methodologies. Another intriguing research direction involves refining network architectures for simultaneous learning of multiple shape categories and enhancing cross-coordinate learning to obviate the need for separate networks.

In conclusion, "SurfNet" presents a sophisticated blend of geometry processing and deep learning, effectively bridging a gap in the generative modeling of 3D shapes with significant potential for refinement and application.