- The paper introduces a novel unsupervised method leveraging differentiable neural sweepers for efficient 3D shape abstraction with minimal parameters.
- It utilizes superellipses and B-spline curves to compactly parameterize complex shapes, outperforming baselines on GC-Object and quadruped datasets.
- The approach enhances shape editing and modeling, offering valuable insights for CAD applications and real-time rendering.
SweepNet: Unsupervised Learning Shape Abstraction via Neural Sweepers
Introduction and Context
The paper introduces SweepNet, a novel approach for shape abstraction utilizing sweep surfaces. The proposed model champions a strategy to achieve shape abstraction using differentiable neural sweepers within an unsupervised learning context. Sweep surfaces, which have been extensively used in computer graphics and vision, serve as the core geometric primitives in SweepNet. These surfaces are defined by sweeping a cross-sectional profile along a predefined axis, allowing the efficient representation of complex shapes with fewer parameters compared to traditional methods.
Methodology
SweepNet leverages superellipses for profile representation and B-spline curves for the sweeping axis, resulting in a compact parameterization that requires as few as 14 floating-point numbers. The B-spline curves offer flexibility in representing the sweeping paths, while superellipses, defined using two radii and a shape curvature parameter, provide a diverse range of profiles from rectangular to star-shaped objects. A quadratic scaling function adds further versatility, enabling dynamic profile adjustments along the sweep.
The model employs an encoder-decoder architecture, where input shapes, voxelized into 3D grids, are processed to predict the parameters of sweep surfaces. A differentiable neural sweeper, trained separately, approximates the swept volume implicit fields, integrating seamlessly into the pipeline. This method circumvents the non-differentiability of traditional sweep surface generation techniques, enabling backpropagation and end-to-end training within the neural network framework.
Results and Comparison
The evaluation of SweepNet was conducted on the custom GC-Object dataset and the quadrupeds dataset. Quantitative metrics such as Chamfer-Distance (CD), Volumetric Intersection over Union (IoU), and F-score were utilized to benchmark the performance. SweepNet demonstrated superior performance across these metrics compared to various baseline methods, including superquadrics and cuboid fittings, neural sketch-and-extrude methods, and constructive solid geometry (CSG) approaches.
- GC-Object Dataset:
- IoU: 0.608
- CD: 0.0168
- F1: 0.985
- Quadrupeds Dataset:
- IoU: 0.482
- CD: 0.0176
- F1: 0.967
These results underscore SweepNet's efficacy in capturing curvy-featuring objects with fewer primitives, showcasing the advantage of the sweep surface representation over traditional parametric and neural profile-based primitives.
Implications and Future Directions
SweepNet's primary contributions lie in its innovative use of sweep surfaces for shape abstraction and its integration of a differentiable neural sweeper, which expands the field of geometric modeling within unsupervised learning frameworks. The method offers a balanced approach to model complexity and detail preservation, challenging existing paradigms in shape abstraction.
Practical Implications:
- Shape Manipulation: The compact parameterization facilitates intuitive and interactive editing, which is beneficial for applications in computer-aided design (CAD) and 3D modeling.
- Adaptability: The flexibility in parameterizing the sweeping axis and profile scaling provides robustness in representing various geometric forms, from simple extrusions to complex organic shapes.
Theoretical Implications:
- Deep Learning Integration: The ability to integrate non-differentiable geometric constructs into neural networks represents a significant step forward. This theoretical advancement may inspire further research into other geometric primitives and their applications in AI.
- Shape Abstraction Efficiency: The method shows potential for applications requiring efficient shape abstraction, such as in real-time graphics rendering and object recognition.
Future Work:
- Complexity Handling: Addressing limitations in representing highly porous or thin structures will be crucial. Future research could explore combining SweepNet with other primitives, such as CSG techniques, to capture finer geometric details.
- Generalization: Extending SweepNet's capabilities to generalize across a broader range of shapes and datasets would enhance its applicability. Incorporating additional input modalities and improving the initialization strategies could mitigate local optima issues.
Conclusion
SweepNet exemplifies a sophisticated approach to shape abstraction through the use of sweep surfaces and differentiable neural sweepers. By offering a compact and versatile parameterization, it achieves high-fidelity shape abstractions with improved editing capabilities. While further research is needed to enhance its applicability, SweepNet represents a significant advancement in the domain of 3D shape abstraction and modeling, bridging gaps between computational geometry and deep learning.
