Proc-GS: Procedural Building Generation for City Assembly with 3D Gaussians (2412.07660v1)

Abstract: Buildings are primary components of cities, often featuring repeated elements such as windows and doors. Traditional 3D building asset creation is labor-intensive and requires specialized skills to develop design rules. Recent generative models for building creation often overlook these patterns, leading to low visual fidelity and limited scalability. Drawing inspiration from procedural modeling techniques used in the gaming and visual effects industry, our method, Proc-GS, integrates procedural code into the 3D Gaussian Splatting (3D-GS) framework, leveraging their advantages in high-fidelity rendering and efficient asset management from both worlds. By manipulating procedural code, we can streamline this process and generate an infinite variety of buildings. This integration significantly reduces model size by utilizing shared foundational assets, enabling scalable generation with precise control over building assembly. We showcase the potential for expansive cityscape generation while maintaining high rendering fidelity and precise control on both real and synthetic cases.

• The paper introduces Proc-GS, a dual-stage framework that integrates procedural generation with 3D Gaussian Splatting for scalable urban modeling.
• It employs a two-stage methodology—asset acquisition and asset assembly—to automate the creation of high-detail 3D building models.
• Results demonstrate superior rendering and compression performance, significantly reducing manual data collection for urban environments.

Procedural Building Generation for City Assembly with 3D Gaussians

The paper presents a significant exploration into the optimization and deployment of procedural generation for architectural modeling, specifically utilizing a novel integration of procedural code with 3D Gaussian Splatting (3D-GS). The authors introduce a framework, named Proc-GS, which leverages the benefits of procedural programming and 3D-GS to address the challenges associated with the high-fidelity replication of repetitive elements in urban environments such as windows and doors, prevalent in the gaming, virtual reality, and filming industries.

Overview and Key Contributions

Proc-GS aims to overcome the limitations of traditional labor-intensive 3D model creations that lack scalability and fidelity by proposing a dual-stage pipeline involving asset acquisition and asset assembly. The unique aspect of the Proc-GS framework lies in its utilization of procedural methods, which are traditionally used in procedural generation (PG) for scalability, integrated into the training process of 3D Gaussian Splatting to significantly optimize the generation of diverse and photo-realistic 3D cityscapes.

The authors introduce the MatrixBuilding Dataset, consisting of dense multi-view images and procedural code for 17 iconic buildings, providing a rich medium for employing their procedural approach. Proc-GS claims to compress the model significantly in terms of Gaussian numbers while maintaining visual quality comparable to traditional methods, as demonstrated through PSNR, SSIM, and LPIPS metrics.

Methodology

The paper delineates a robust two-stage method for procedural generation.

1. Asset Acquisition: This stage involves the automatic extraction of 3D base assets facilitated by specific procedural codes derived from existing assets or obtained from real-world scenes using the 2D-GS framework for accurate modeling of building facades.
2. Asset Assembly: Utilizing the procedural code, the framework permits vast diversity in architectural modeling by strategically manipulating these base assets. The procedural codes serve as a template for generating new building geometries with varying architectural details and scales, providing an efficient mechanism for expanding virtual city models.

Significantly, the methodology addresses a common challenge in procedural city modeling—ensuring high fidelity in view-dependent rendering without compromising geometric precision, attributes usually hampered in procedural techniques when applied at scale.

Results and Implications

The Proc-GS framework is said to outperform existing methods by establishing superior rendering and geometry quality in synthetic and real-world settings while maintaining efficient real-time processing capabilities. Interestingly, the document highlights the profound capability of Proc-GS when processing from sparse view inputs, hence reducing the data collection labor typically required in similar setups significantly.

From a broader scientific perspective, this work signifies a step towards more efficient, procedural-based methodologies in procedural modeling, with significant applications across various domains including game development, films, and simulations in autonomous driving and AI emulation environments.

Future Directions

The paper's foundation opens several avenues for future research and development. One key area is the full automation of procedural code generation from real-world data, which can further minimize human intervention. Additionally, while current implementations rely heavily on rule-based approaches for city layout generation, integrating machine learning techniques like LLMs could introduce aesthetically and functionally superior designs.

In summary, Proc-GS appears to offer a viable enhancement to procedural modeling through its novel integration with 3D Gaussian Splatting, demonstrating a considerable leap in generating hyper-realistic urban environments. The framework not only illustrates potential advancements in rendering techniques but also paves the way for practical applications where high-quality structured environmental data is crucial.