Available Projects
Available Projects
Enhancing 3D Mesh Neural Cellular Automata with Frequency Decomposition
Introduction
3D mesh generation plays a pivotal role in virtual reality, gaming, and digital content creation, but generating high-quality, detailed meshes remains a challenging task. Traditional methods often fail to capture fine-grained details or optimize computational efficiency, especially for complex, textured surfaces. This proposal seeks to enhance 3D mesh generation by incorporating frequency decomposition models, leveraging multi-resolution analysis to capture both broad structural features and intricate details.
Objective
The primary goal of this research is to develop a frequency-based decomposition model for 3D mesh generation, enabling precise control over the detail level of generated meshes. By decomposing spatial and frequency components, we aim to improve mesh quality, reduce processing times, and enhance texture and surface detail.
Methodology
Frequency Decomposition: Apply discrete wavelet transforms (DWT) on the spatial and normal maps of 3D meshes, separating high-frequency components (surface details) from low-frequency components (broad structural shapes).
Component-specific Optimization: Tailor the mesh generation model to optimize specific frequency components. For example, low-frequency structures can be prioritized for smooth topology, while high-frequency details can be preserved in texture-rich areas.
Multi-level Reconstruction: Iteratively reconstruct the mesh from frequency components using an inverse wavelet transform (IDWT), allowing for customizable levels of detail depending on the desired quality.
Evaluation and Benchmarking: Compare the proposed approach against existing methods on benchmarks, measuring structural consistency, texture fidelity, and computational efficiency.
Expected Contributions
A novel, frequency-based approach for enhancing 3D mesh quality.
A multi-level decomposition and reconstruction framework that allows selective detail optimization.
An efficient algorithm capable of handling complex surfaces without compromising mesh detail.
Prerequisites: Python and PyTorch. Basic understanding of diffusion models.
Level: MS research project
Number of students: 1/2
Contact: Zicong Jiang, zicongj@chalmers.se
Optical Fiber Modeling with Diffusion ModelÂ
Introduction
3D mesh generation plays a pivotal role in virtual reality, gaming, and digital content creation, but generating high-quality, detailed meshes remains a challenging task. Traditional methods often fail to capture fine-grained details or optimize computational efficiency, especially for complex, textured surfaces. This proposal seeks to enhance 3D mesh generation by incorporating frequency decomposition models, leveraging multi-resolution analysis to capture both broad structural features and intricate details.
Objective
The primary goal of this research is to develop a frequency-based decomposition model for 3D mesh generation, enabling precise control over the detail level of generated meshes. By decomposing spatial and frequency components, we aim to improve mesh quality, reduce processing times, and enhance texture and surface detail.
Methodology
Frequency Decomposition: Apply discrete wavelet transforms (DWT) on the spatial and normal maps of 3D meshes, separating high-frequency components (surface details) from low-frequency components (broad structural shapes).
Component-specific Optimization: Tailor the mesh generation model to optimize specific frequency components. For example, low-frequency structures can be prioritized for smooth topology, while high-frequency details can be preserved in texture-rich areas.
Multi-level Reconstruction: Iteratively reconstruct the mesh from frequency components using an inverse wavelet transform (IDWT), allowing for customizable levels of detail depending on the desired quality.
Evaluation and Benchmarking: Compare the proposed approach against existing methods on benchmarks, measuring structural consistency, texture fidelity, and computational efficiency.
Expected Contributions
A novel, frequency-based approach for enhancing 3D mesh quality.
A multi-level decomposition and reconstruction framework that allows selective detail optimization.
An efficient algorithm capable of handling complex surfaces without compromising mesh detail.
Prerequisites: Python and PyTorch. Basic understanding of diffusion models.
Level: MS research project
Number of students: 1/2
Contact: Zicong Jiang, zicongj@chalmers.se