3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models (2301.11445v3)

Abstract: We introduce 3DShape2VecSet, a novel shape representation for neural fields designed for generative diffusion models. Our shape representation can encode 3D shapes given as surface models or point clouds, and represents them as neural fields. The concept of neural fields has previously been combined with a global latent vector, a regular grid of latent vectors, or an irregular grid of latent vectors. Our new representation encodes neural fields on top of a set of vectors. We draw from multiple concepts, such as the radial basis function representation and the cross attention and self-attention function, to design a learnable representation that is especially suitable for processing with transformers. Our results show improved performance in 3D shape encoding and 3D shape generative modeling tasks. We demonstrate a wide variety of generative applications: unconditioned generation, category-conditioned generation, text-conditioned generation, point-cloud completion, and image-conditioned generation.

• The paper introduces a novel 3D shape representation that encodes shapes as fixed-length latent sets using cross-attention and diffusion models.
• The methodology employs a two-stage training strategy—a dedicated autoencoder followed by a latent diffusion process—resulting in superior reconstruction fidelity.
• Applications span high-fidelity 3D generation for VR, CAD, and gaming, enabling category- and text-conditioned synthesis.

Advancing 3D Shape Generation with 3DShape2VecSet

Introduction to 3DShape2VecSet

The domain of 3D content generation has witnessed significant advancements, driven by the adoption of generative models such as GANs, VAEs, and diffusion models. One of the challenges within this area is the effective representation of 3D shapes, which is critical for tasks including shape reconstruction, completion, and generative modeling. Addressing this, researchers Biao Zhang, Jiapeng Tang, Matthias Nießner, and Peter Wonka introduced 3DShape2VecSet, a novel shape representation designed specifically for neural fields and generative diffusion models.

Key Contributions

3DShape2VecSet presents a comprehensive approach for encoding and generating 3D shapes via a set of latent vectors, leveraging radial basis function representations, cross-attention, and self-attention mechanisms, synonymous with transformer networks. This framework demonstrates remarkable improvements in both encoding quality and shape generation tasks.

The contributions can be summarized as follows:

• A new 3D shape representation that encodes any given shape as a fixed-length array of latents.
• A novel network architecture utilizing cross-attention and linear layers for processing shapes in the proposed representation.
• Advancements in 3D shape autoencoding, demonstrating superior fidelity in shape reconstruction.
• A latent set diffusion framework that surpasses existing methods in 3D shape generation quality.
• Diverse applications of the model, including category- and text-conditioned generation, point-cloud completion, and image-conditioned generation.

Methodology

The authors propose a two-stage training strategy, initially involving an autoencoder framework to generate a condensed latent space representation, followed by training a diffusion model within this latent space. This approach effectively addresses the challenges posed by the continuous and high-dimensional nature of 3D shapes. Furthermore, by leveraging learnable representations over manually designed ones, 3DShape2VecSet can adaptively capture intricate shape details.

Theoretical Implications

From a theoretical perspective, this work deepens our understanding of latent space manipulations for 3D objects. The shift from traditional spatial representations to a set-based latent approach introduces a novel paradigm for efficiently managing the complexity inherently associated with 3D shapes. Moreover, the model's ability to learn positional information without explicit spatial anchoring suggests potential applications beyond shape generation, possibly extending to other domains where spatial relationships are critical.

Practical Applications

Practically, 3DShape2VecSet significantly advances the capability for high-fidelity 3D shape generation, which is a critical need in industries such as gaming, virtual reality, and computer-aided design (CAD). By delivering improved performance in both shape reconstruction and diverse generative tasks, the proposed framework broadens the horizon for creating realistic and complex 3D content, auguring well for future content creation pipelines.

Future Directions

Looking forward, this research opens several avenues worth exploring. One area of interest could be investigating the potential for real-time generation and manipulation of 3D shapes, offering interactive applications such as virtual sculpting tools. Additionally, extending the representation to include texture and material properties could further enhance the utility of generated models, making them ready for direct use in visualization and simulation applications.

Conclusion

In conclusion, 3DShape2VecSet marks a significant step forward in the field of 3D shape generation. By effectively addressing the representation and generation challenges, this work not only advances the state-of-the-art but also paves the way for novel applications and methodologies in 3D content creation. The implications of this research are broad, impacting both theoretical and practical aspects of computer graphics and beyond.