L3DG: Latent 3D Gaussian Diffusion (2410.13530v1)

Abstract: We propose L3DG, the first approach for generative 3D modeling of 3D Gaussians through a latent 3D Gaussian diffusion formulation. This enables effective generative 3D modeling, scaling to generation of entire room-scale scenes which can be very efficiently rendered. To enable effective synthesis of 3D Gaussians, we propose a latent diffusion formulation, operating in a compressed latent space of 3D Gaussians. This compressed latent space is learned by a vector-quantized variational autoencoder (VQ-VAE), for which we employ a sparse convolutional architecture to efficiently operate on room-scale scenes. This way, the complexity of the costly generation process via diffusion is substantially reduced, allowing higher detail on object-level generation, as well as scalability to large scenes. By leveraging the 3D Gaussian representation, the generated scenes can be rendered from arbitrary viewpoints in real-time. We demonstrate that our approach significantly improves visual quality over prior work on unconditional object-level radiance field synthesis and showcase its applicability to room-scale scene generation.

• The paper introduces L3DG, a latent 3D Gaussian diffusion framework that advances generative modeling for high-fidelity room-scale scenes.
• The method employs a VQ-VAE and sparse convolution to encode 3D Gaussian primitives into a compressed latent space, reducing computational cost.
• Experimental results show a 45% improvement in FID over DiffRF, demonstrating its viability for real-time applications in VR, gaming, and architectural visualization.

Latent 3D Gaussian Diffusion for Generative Scene Modeling

The paper introduces L3DG, a novel framework for generative 3D modeling, leveraging a latent 3D Gaussian diffusion approach. The core objective of this research is to enhance the generative modeling of 3D scenes by employing 3D Gaussians, enabling the generation of scalable and high-fidelity room-scale scenes. This is facilitated through a compression technique utilizing a vector-quantized variational autoencoder (VQ-VAE), creating a latent space where diffusion processes can be effectively utilized.

Methodology

L3DG builds upon the principles of 3D Gaussian splatting, where 3D Gaussian primitives act as the infrared scene representation. By adopting a variational autoencoder mechanism, the model shifts these primitives into a latent space using a sparse convolutional network, specifically optimized for room-scale rendering. This approach drastically reduces the computational complexity typically associated with diffusion processes in large and detailed scenes.

The proposed model incorporates a sparse grid representation for these 3D Gaussians, allowing for efficient operations in high-dimensional spaces, using a VQ-VAE to learn a compressed latent space. The diffusion model operates within this learned latent space, which unifies the irregularly structured 3D Gaussians into a coherent generative framework. The process is further refined by training the generation process through a series of denoising steps, optimizing the model's capability to produce high-detail object-level and room-scale scenes.

Results and Evaluation

Quantitative evaluations reveal significant improvements compared to prior works. For instance, L3DG shows a 45% improvement in the FID metric over DiffRF on the PhotoShape dataset. These results not only indicate enhanced synthesis quality but also demonstrate the model's ability to manage large volumetric data efficiently.

In terms of practical implications, the model's capability to render scenes in real-time from arbitrary viewpoints is particularly noteworthy, catering to industries requiring rapid and realistic scene generation such as gaming, virtual reality, and architectural visualization. The scalability feature of L3DG provides a versatile approach for both high-fidelity object generation and elaborate room-scale scene synthesis.

Implications and Future Directions

This work suggests several theoretical and practical implications. Theoretically, merging diffusion models with Gaussian representations opens new avenues in probabilistic modeling for graphics. Practically, the advancements in speed and scalability may redefine real-time rendering processes across various applications.

Future work might explore refining the sparse convolutional architecture for even more efficient encoding and decoding processes. Furthermore, incorporating real-world datasets with high-fidelity captures could validate the model's applicability beyond synthetic environments.

L3DG stands as a promising advancement in generative 3D modeling, offering solutions for the complexities of high-detailed scene synthesis while maintaining computational efficiency. This work not only advances current methodologies but also lays the foundation for future innovations in AI-driven 3D content creation.