Learning Implicit Fields for Generative Shape Modeling (1812.02822v5)

Abstract: We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes. An implicit field assigns a value to each point in 3D space, so that a shape can be extracted as an iso-surface. IM-NET is trained to perform this assignment by means of a binary classifier. Specifically, it takes a point coordinate, along with a feature vector encoding a shape, and outputs a value which indicates whether the point is outside the shape or not. By replacing conventional decoders by our implicit decoder for representation learning (via IM-AE) and shape generation (via IM-GAN), we demonstrate superior results for tasks such as generative shape modeling, interpolation, and single-view 3D reconstruction, particularly in terms of visual quality. Code and supplementary material are available at https://github.com/czq142857/implicit-decoder.

• The paper introduces IM-NET, an implicit field decoder that significantly improves automatic 3D shape encoding and reconstruction using a multi-layer perceptron architecture.
• The methodology employs progressive training to achieve high-resolution outputs and demonstrates superior performance over voxel- and point cloud-based methods.
• The approach excels in generative modeling and single-view 3D reconstruction, offering smoother shape interpolation and enhanced visual quality.

Learning Implicit Fields for Generative Shape Modeling

"Learning Implicit Fields for Generative Shape Modeling" by Zhiqin Chen and Hao Zhang introduces a novel approach to 3D shape generation by leveraging implicit fields. The methodology focuses on improving visual quality of generated shapes through an implicit field decoder named IM-NET. This decoder assigns a continuous value to each point in 3D space, defining a shape as an iso-surface. The key innovation is using the implicit field within deep learning architectures for generative modeling, achieving superior results in automatic shape encoding, generation, interpolation, and single-view 3D reconstruction.

Key Insights and Methodology

An implicit field in this context is a continuous function that defines whether a point in space is inside or outside a given shape. IM-NET employs a multi-layer perceptron (MLP) to learn this implicit representation. By inputting point coordinates and a shape feature vector, the decoder predicts the inside/outside status of each point, facilitating high-resolution shape generation unconstrained by the resolution of training data.

Key contributions of the paper include:

1. Implicit Field Decoder (IM-NET):
   • IM-NET assigns values to spatial coordinates to define shapes implicitly.
   • The simplicity of MLPs ensures the network can learn the inside/outside assignment effectively.
   • Progressive training is used, starting with low-resolution data and gradually increasing resolution, ensuring stability and faster convergence.
2. Neural Network Architecture:
   • A simple architecture is adopted, incorporating point coordinates with shape features to learn the implicit field.
   • The decoder predicts point-wise values allowing for high-resolution outputs (e.g., up to 512^3).
3. Evaluation Metrics and Results:
   • Evaluation metrics include Chamfer Distance (CD), Intersection over Union (IoU), and Light Field Descriptor (LFD).
   • IM-NET exhibits superior visual quality compared to state-of-the-art methods such as 3DGAN and point cloud-based methods.

Experimental Validation

Auto-Encoding and Shape Reconstruction

The performance of IM-NET in reconstructing 3D shapes is evaluated by comparing it with 3D CNN-based autoencoders. IM-NET demonstrates higher fidelity in capturing shape details. Notably, metrics like LFD show that IM-NET produces cleaner and more precise surfaces compared to CNN-based approaches. This was particularly evident in tasks such as generating thin table boards, which are distinctly visible in high-resolution samples.

Shape Generation and Interpolation

IM-NET's capabilities extend beyond reconstruction to generative shape modeling using GANs (IM-GAN). Compared to 3DGAN and other point cloud-based methods, IM-GAN provides higher coverage and lower minimum matching distances, indicating a broader diversity and accurate generation of shape samples. The interpolation results showcase IM-NET's ability to handle complex shape changes and topological transitions smoothly.

Single-View 3D Reconstruction (SVR)

The paper also benchmarks IM-NET in single-view 3D reconstruction against leading methods like HSP and AtlasNet. IM-NET's reconstruction quality, particularly using LFD, demonstrates its ability to produce visually plausible and high-quality meshes, outperforming AtlasNet in categories requiring complex topologies.

Theoretical and Practical Implications

IM-NET's architecture represents a significant step towards generating high-resolution and detailed 3D shapes with reduced computational overhead in generating outputs. By reformulating 3D shape encoding as a binary classification problem within an implicit field, the approach shifts away from voxel-based and mesh-based decoders, leading to better generalization and compact representations.

Future Developments

Potential avenues for future research include:

1. Enhanced Decoder Architectures: Optimizing the MLP structure to reduce training and inference time without sacrificing output quality.
2. Comprehensive Shape Attributes: Extending the implicit field to encode additional properties such as texture, color, and surface normals for advanced representations.
3. Cross-Category Generalization: Exploring the generalization of IM-NET across diverse shape categories to assess its flexibility and robustness.

Conclusion

The contribution by Chen and Zhang presents a robust framework for generative shape modeling using implicit fields. IM-NET's ability to produce high-quality, smooth, and topologically consistent 3D shapes underscores its potential in applications ranging from virtual reality to advanced manufacturing. The work opens new dimensions for leveraging implicit representations in deep learning to achieve scalable and efficient 3D shape generation.