NeuSDFusion: A Spatial-Aware Generative Model for 3D Shape Completion, Reconstruction, and Generation

Temel Kavramlar

Introducing a novel spatial-aware 3D shape generation framework leveraging hybrid shape representation for enhanced modeling.

Özet

The content introduces NeuSDFusion, a novel spatial-aware 3D shape generation framework that leverages 2D plane representations for enhanced 3D shape modeling. It addresses the limitations of existing methods by incorporating a hybrid shape representation technique that directly learns a continuous signed distance field representation of the 3D shape using orthogonal 2D planes. The framework ensures spatial coherence and reduces memory usage by enforcing spatial correspondences across distinct planes using a transformer-based autoencoder structure. NeuSDFusion consistently outperforms state-of-the-art 3D shape generation methods on various tasks, including unconditional shape generation, multi-modal shape completion, single-view reconstruction, and text-to-shape synthesis.

Introduction

• Existing methods decompose 3D shapes into localized components.
• Spatial consistency is crucial for versatile 3D data representation.
• NeuSDFusion introduces a spatial-aware 3D shape generation framework.

Data Representation

• Hybrid shape representation technique for enhanced 3D modeling.
• Continuous signed distance field representation using orthogonal 2D planes.
• Incorporation of a transformer-based autoencoder structure for spatial coherence.

Performance Evaluation

• Outperforms state-of-the-art methods in various 3D shape generation tasks.
• Demonstrates superior performance in unconditional generation and shape completion.
• Achieves high-quality 3D shape reconstruction from single-view images.

İstatistikler

Existing methods often overlook spatial coherence.
NeuSDFusion consistently outperforms state-of-the-art methods.

Alıntılar

"Our method consistently outperforms existing techniques, achieving state-of-the-art results in unconditional generation."
"Our approach excels across all categories, demonstrating the significant superiority of our proposed pipeline in the detailed modeling of 3D shapes."

Önemli Bilgiler Şuradan Elde Edildi

NeuSDFusion

by Ruikai Cui,W... : arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18241.pdf

Daha Derin Sorular

How can the spatial-aware autoencoder design be further optimized for efficiency

To further optimize the spatial-aware autoencoder design for efficiency, several strategies can be implemented:

Sparse Attention Mechanisms: Implementing sparse attention mechanisms can reduce computational complexity by focusing on relevant parts of the input, thus improving efficiency.
Quantization Techniques: Utilizing quantization techniques can reduce the memory footprint of the model, making it more efficient for deployment on resource-constrained devices.
Knowledge Distillation: Employing knowledge distillation techniques can transfer the knowledge learned by a larger, more complex model to a smaller, more efficient model, without compromising performance.
Parallel Processing: Utilizing parallel processing techniques can distribute the computational load across multiple processing units, improving overall efficiency.
Model Pruning: Removing redundant or less important parameters from the model through model pruning can significantly reduce the model size and improve efficiency without sacrificing performance.

What are the potential real-world applications of NeuSDFusion beyond 3D shape generation

The potential real-world applications of NeuSDFusion beyond 3D shape generation are diverse and impactful:

Medical Imaging: NeuSDFusion can be used for medical imaging applications, such as reconstructing 3D structures from 2D medical scans, aiding in diagnosis and treatment planning.
Virtual Reality and Gaming: In the virtual reality and gaming industries, NeuSDFusion can be utilized for creating realistic and detailed 3D environments, characters, and objects, enhancing user experience.
Architectural Design: Architects and designers can leverage NeuSDFusion for creating detailed 3D models of buildings and structures, facilitating visualization and planning.
Manufacturing and Prototyping: NeuSDFusion can be applied in manufacturing and prototyping processes to generate accurate 3D models for product design and development.
Robotics: In robotics, NeuSDFusion can assist in creating 3D models of environments for navigation and object manipulation tasks, improving the efficiency and accuracy of robotic systems.

How can the concept of spatial coherence in 3D shape modeling be applied to other domains

The concept of spatial coherence in 3D shape modeling can be applied to other domains beyond computer vision and graphics:

Biomedical Imaging: In biomedical imaging, spatial coherence can be utilized to reconstruct detailed 3D structures from medical scans, aiding in disease diagnosis and treatment planning.
Geospatial Analysis: Spatial coherence can be applied in geospatial analysis to model and analyze 3D terrain data, improving the accuracy of geographical mapping and environmental monitoring.
Material Science: In material science, spatial coherence can help in modeling complex 3D structures of materials at the atomic level, enhancing research in material properties and behavior.
Augmented Reality: Spatial coherence can be leveraged in augmented reality applications to create realistic and consistent 3D overlays in the physical environment, enhancing user interaction and immersion.
Urban Planning: Spatial coherence can assist in creating accurate 3D models of urban environments for urban planning and development, optimizing infrastructure design and resource allocation.