Constructing a 3D Town from a Single Image (2505.15765v1)

Abstract: Acquiring detailed 3D scenes typically demands costly equipment, multi-view data, or labor-intensive modeling. Therefore, a lightweight alternative, generating complex 3D scenes from a single top-down image, plays an essential role in real-world applications. While recent 3D generative models have achieved remarkable results at the object level, their extension to full-scene generation often leads to inconsistent geometry, layout hallucinations, and low-quality meshes. In this work, we introduce 3DTown, a training-free framework designed to synthesize realistic and coherent 3D scenes from a single top-down view. Our method is grounded in two principles: region-based generation to improve image-to-3D alignment and resolution, and spatial-aware 3D inpainting to ensure global scene coherence and high-quality geometry generation. Specifically, we decompose the input image into overlapping regions and generate each using a pretrained 3D object generator, followed by a masked rectified flow inpainting process that fills in missing geometry while maintaining structural continuity. This modular design allows us to overcome resolution bottlenecks and preserve spatial structure without requiring 3D supervision or fine-tuning. Extensive experiments across diverse scenes show that 3DTown outperforms state-of-the-art baselines, including Trellis, Hunyuan3D-2, and TripoSG, in terms of geometry quality, spatial coherence, and texture fidelity. Our results demonstrate that high-quality 3D town generation is achievable from a single image using a principled, training-free approach.

Constructing a 3D Town from a Single Image: An In-Depth Analysis

In contemporary research on 3D scene generation, the demand for realistic and efficient 3D modeling technologies is significant, particularly in applications related to simulation, digital content creation, and virtual world building. The paper titled "Constructing a 3D Town from a Single Image" by Kaizhi Zheng et al. introduces an innovative approach called "3DTown," which offers a novel methodology for synthesizing 3D scenes from a solitary top-down image without the need for 3D training. This paper explores the unique challenges inherent to 3D scene synthesis, contrasting current practices, and presenting a method that potentially rectifies existing limitations.

Technical Framework and Contributions

The paper delineates a structured approach designated as 3DTown, which leverages region-based generation and spatial-awareness via 3D inpainting. The methodology is principally focused on:

1. Region-Based Generation: The input image is segmented into overlapping regions, each independently processed using a pretrained 3D object generator. This division enhances the resolution and coherence of the image-to-3D alignment, addressing typical multiview inconsistency issues encountered in previous models.
2. Spatial-Awareness via 3D Inpainting: This inpainting approach ensures the generation maintains a globally coherent spatial structure and detailed geometry. It involves a masked rectified flow inpainting mechanism that fills gaps in the geometry to maintain the scene's integrity and spatial continuity.

The innovative aspect of 3DTown lies in mitigating constraints posed by existing approaches that often necessitate extensive datasets, training, or expensive equipment for effective 3D model generation. Unlike methods relying on Neural Radiance Fields or 3D Gaussian Splatting, which struggle with occlusions and texture alignment, 3DTown effectively addresses these hindrances by preserving spatial relationships and improving local object fidelity.

Empirical Validation

The qualitative and quantitative evaluations of 3DTown, as conducted in the paper, indicate its superiority over state-of-the-art models such as Trellis, Hunyuan3D-2, and TripoSG. Notably, the experiments demonstrated that 3DTown achieved improved geometry quality, layout coherence, and texture fidelity, with extensive assessments illustrating significant gains. Human preference and GPT-based evaluations underscored these findings, with 3DTown displaying a more accurate and semantically coherent 3D scene synthesis from single image inputs.

Practical and Theoretical Implications

The implications of this work extend across various fields needing efficient 3D content generation. This advancement not only opens pathways for cost-effective and accessible 3D modeling but also lays the groundwork for future developments in AI-based scene synthesis. With no training required, 3DTown demonstrates the feasibility of leveraging modular, structured processes that enhance the quality of 3D asset generation while preserving computational efficiency.

Future Directions and Limitations

Looking forward, it is essential to explore enhancements in the robustness and adaptability of the 3DTown framework, particularly in handling diverse and complex real-world scenes. The method could benefit from incorporating additional datasets to address limitations pertaining to varied architectural and terrain features which could lead to more universally adaptable applications.

Moreover, the exploration of further integrating machine learning models focused on domain adaptation and transfer learning might yield advances in scene recognition and reconstruction, potentially automating more aspects of 3D modeling.

Conclusion

3DTown emerges as a significant step forward in generating coherent, realistic 3D scenes from minimal input data, specifically a single image. The seamless integration of region-based frameworks and spatially-aware inpainting mechanisms addresses critical challenges in current 3D scene generation techniques while setting a new standard for future research and application in AI-driven modeling processes.