LT3SD: Latent Trees for 3D Scene Diffusion (2409.08215v1)

Abstract: We present LT3SD, a novel latent diffusion model for large-scale 3D scene generation. Recent advances in diffusion models have shown impressive results in 3D object generation, but are limited in spatial extent and quality when extended to 3D scenes. To generate complex and diverse 3D scene structures, we introduce a latent tree representation to effectively encode both lower-frequency geometry and higher-frequency detail in a coarse-to-fine hierarchy. We can then learn a generative diffusion process in this latent 3D scene space, modeling the latent components of a scene at each resolution level. To synthesize large-scale scenes with varying sizes, we train our diffusion model on scene patches and synthesize arbitrary-sized output 3D scenes through shared diffusion generation across multiple scene patches. Through extensive experiments, we demonstrate the efficacy and benefits of LT3SD for large-scale, high-quality unconditional 3D scene generation and for probabilistic completion for partial scene observations.

• The paper introduces a latent tree-based diffusion model that hierarchically encodes complex 3D scene geometry.
• It employs a patch-based training strategy to synthesize arbitrarily large 3D scenes with enhanced detail and computational efficiency.
• The method improves FID by 70% over existing models, offering significant potential for VR/AR and automated 3D content creation.

An Analysis of "LT3SD: Latent Trees for 3D Scene Diffusion"

The paper "LT3SD: Latent Trees for 3D Scene Diffusion," authored by Quan Meng, Lei Li, Matthias Nießner, and Angela Dai from the Technical University of Munich, presents an innovative approach for the generation of large-scale 3D scenes using a latent diffusion model. The proposed method attempts to address the limitations of existing 3D diffusion models, particularly their struggles with spatial extent and quality in 3D scene generation, by introducing a novel latent tree representation.

Key Contributions

The paper introduces a novel latent 3D scene diffusion approach that leverages a hierarchical latent tree representation to effectively encode complex 3D scene geometry. This method provides high-fidelity 3D scene generation using a patch-by-patch and coarse-to-fine approach along the latent tree resolution levels. The primary contributions of the paper are:

1. Latent Tree Representation: The introduction of a hierarchical latent tree structure to decompose 3D scenes into geometric and latent feature components, enabling more compact and efficient diffusion modeling.
2. Patch-wise Scene Generation: A methodology to synthesize infinite 3D scenes by training the diffusion model on scene patches, which enhances computational efficiency and allows for arbitrary-sized scene generation.

Methodology

The approach is divided into two main stages: Latent Tree Encoding and Patch-Based Latent Scene Diffusion.

Latent Tree Encoding

In this stage, the 3D scene is decomposed into a hierarchical structure of TUDF (Truncated Unsigned Distance Field) grids representing coarse geometry and latent feature grids capturing higher-frequency details. This decomposition is performed using a 3D Convolutional Neural Network (CNN) that factors the scene into progressively lower resolution levels, facilitating both efficient storage and training. Each level in the tree structure is independently trained using mean squared error loss to ensure accurate reconstruction of 3D scenes.

Patch-Based Latent Scene Diffusion

The second stage involves training a UNet-based diffusion model to generate the latent features, conditioned on the coarse geometry patches. This stage leverages the patch-wise training strategy to effectively capture local structures and enhance generalization. During inference, the method synthesizes large-scale scenes by progressively generating the latent trees patch-by-patch from coarse to fine levels, ensuring geometric coherency and rich detail.

Experimental Validation

The authors validate the efficacy of LT3SD on the 3D-FRONT dataset, a large-scale dataset of diverse indoor scenes. The results indicate that LT3SD significantly outperforms existing 3D diffusion models, including PVD, NFD, and BlockFusion, in both quantitative and qualitative analyses. The model improves Fréchet Inception Distance (FID) scores by 70%, showcasing its superior ability to generate high-fidelity 3D scenes with coherent structures and detailed geometry.

Quantitatively, the model demonstrated marked improvements in Coverage (COV), Minimum Matching Distance (MMD), and 1-Nearest Neighbor Accuracy (1-NNA) metrics, which are critical for assessing diversity, fidelity, and distribution similarity of generated scenes. Notably, the hierarchical latent tree approach enabled the model to synthesize unseen structures and layouts, highlighting its generalizability and capacity to capture diverse scene configurations.

Implications and Future Work

The proposed LT3SD method has significant implications for the fields of VR/AR, video game development, and any domain requiring automated high-quality 3D content creation. Its ability to generate large-scale, high-resolution 3D scenes efficiently is particularly valuable in reducing the time and cost associated with manual 3D modeling.

Theoretically, the hierarchical latent tree representation introduces a scalable approach for complex 3D scene generation, addressing the limitations of extant methods which often fail to generalize beyond fixed and compact object-level representations.

Future directions for this research could involve integrating semantic understanding to further enhance scene realism and coherence. Additionally, exploring the applicability of this method to outdoor or mixed indoor-outdoor environments may unlock new possibilities for urban modeling and landscape design. Enhancing the efficiency of the model, perhaps through more advanced parallelization techniques, could also expand its utility in real-time applications.

Conclusion

The LT3SD paper presents a robust framework for 3D scene generation, leveraging a latent tree-based hierarchical representation to achieve high-fidelity, large-scale outputs. The method's performance, as substantiated by extensive empirical analyses, underscores its potential to significantly advance the automation of 3D content creation. The innovative architecture and methodology proposed offer a compelling avenue for future exploration and application in the growing demand for realistic, computable 3D environments.