Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models (2311.17050v3)

Abstract: We present Surf-D, a novel method for generating high-quality 3D shapes as Surfaces with arbitrary topologies using Diffusion models. Previous methods explored shape generation with different representations and they suffer from limited topologies and poor geometry details. To generate high-quality surfaces of arbitrary topologies, we use the Unsigned Distance Field (UDF) as our surface representation to accommodate arbitrary topologies. Furthermore, we propose a new pipeline that employs a point-based AutoEncoder to learn a compact and continuous latent space for accurately encoding UDF and support high-resolution mesh extraction. We further show that our new pipeline significantly outperforms the prior approaches to learning the distance fields, such as the grid-based AutoEncoder, which is not scalable and incapable of learning accurate UDF. In addition, we adopt a curriculum learning strategy to efficiently embed various surfaces. With the pretrained shape latent space, we employ a latent diffusion model to acquire the distribution of various shapes. Extensive experiments are presented on using Surf-D for unconditional generation, category conditional generation, image conditional generation, and text-to-shape tasks. The experiments demonstrate the superior performance of Surf-D in shape generation across multiple modalities as conditions. Visit our project page at https://yzmblog.github.io/projects/SurfD/.

• The paper introduces Surf-D, which uses a UDF-based diffusion model to overcome limitations in generating complex 3D surfaces with arbitrary topologies.
• It employs a point-based AutoEncoder combined with curriculum learning to efficiently encode shapes and produce detailed geometric structures.
• Experiments demonstrate that Surf-D outperforms earlier methods in quality and computational efficiency in tasks like unconditional and text-guided 3D generation.

An Essay on the Paper "Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models"

The paper presents a novel method called Surf-D, which tackles the problem of generating high-quality 3D shapes encoded as surfaces with arbitrary topologies using diffusion models. Unlike previous methodologies that have faced challenges due to limited topology representation or inferior geometric detailing, Surf-D introduces an innovative approach by utilizing this inherent versatility in Unsigned Distance Fields (UDFs) to represent surfaces effectively. This approach not only improves the geometric quality and topological variety of generated surfaces but also aligns surface learning with a scalable and efficient computational model.

Methodology

The complexity of the task at hand necessitated a robust and adaptable surface representation. Previous methods leveraged point clouds, voxel grids, and SDFs, each plagued by limitations regarding resolution, precision, and topology adaptability. Surf-D circumvents these limitations by leveraging UDFs, a less restrictive representation that supports arbitrary topologies and thereby fosters a variety of geometric features.

Surf-D implements a point-based AutoEncoder and a latent diffusion model as the core components of its architecture. The AutoEncoder facilitates efficient shape encoding, transforming sampled surface points into a compact latent space representation. This approach emphasizes learning continuous fields as opposed to discrete, grid-based embeddings that inherently limit precision and scalability.

The framework employs a curriculum learning strategy, which advances the model's training by incrementally integrating samples from an easy-to-hard regime. This progressive training promotes more effective learning across diverse shape distributions and diminishes the risk of model collapse.

Key Findings and Results

The primary contribution lies in demonstrating that Surf-D yields superior performance across various shape generation tasks, showcasing exceptional results in unconditional generation, category-conditional generation, sketch-guided shape generation, single-view 3D reconstruction, and text-to-shape tasks. The experiments conducted reveal that Surf-D can significantly surpass preceding methods, both quantitatively and qualitatively.

Notably, Surf-D adds considerable computational advantages, requiring less memory due to its efficient encoding mechanism, while still allowing high-resolution output. The point-based representation promotes robustness and precision in gradient field computations, enabling the generation of intricate geometries unattainable by grid-based representations.

Implications and Future Work

Surf-D lays the groundwork for future exploration in utilizing diffusion models for diverse applications in computer graphics, gaming, and simulation. This research invites future work in further optimizing mesh extraction from UDF fields, possibly exploring gradient-free methodologies for efficiency gains.

Moreover, the versatility of the UDF representation could be tap into broader AI applications, where understanding content topology without severe computational overhead becomes necessary. The utilization of curriculum learning strategies also opens avenues for disentangling complex data distributions through structured data integration.

Further investigations could exploit Surf-D in augmented reality environments, enhancing virtual try-on systems, or amalgamating visual and textual data for robust multimodal shape generation.

In summary, Surf-D presents an impactful contribution to the field of 3D shape generation, significantly advancing the quality and diversity of generated surfaces. The innovative application of diffusion models to surface geometry challenges extends the possibilities of creating detailed and complex topologies with high precision, offering a promising outlook for future research and development.