Scene4U: Hierarchical Layered 3D Scene Reconstruction from Single Panoramic Image for Your Immerse Exploration (2504.00387v2)

Abstract: The reconstruction of immersive and realistic 3D scenes holds significant practical importance in various fields of computer vision and computer graphics. Typically, immersive and realistic scenes should be free from obstructions by dynamic objects, maintain global texture consistency, and allow for unrestricted exploration. The current mainstream methods for image-driven scene construction involves iteratively refining the initial image using a moving virtual camera to generate the scene. However, previous methods struggle with visual discontinuities due to global texture inconsistencies under varying camera poses, and they frequently exhibit scene voids caused by foreground-background occlusions. To this end, we propose a novel layered 3D scene reconstruction framework from panoramic image, named Scene4U. Specifically, Scene4U integrates an open-vocabulary segmentation model with a LLM to decompose a real panorama into multiple layers. Then, we employs a layered repair module based on diffusion model to restore occluded regions using visual cues and depth information, generating a hierarchical representation of the scene. The multi-layer panorama is then initialized as a 3D Gaussian Splatting representation, followed by layered optimization, which ultimately produces an immersive 3D scene with semantic and structural consistency that supports free exploration. Scene4U outperforms state-of-the-art method, improving by 24.24% in LPIPS and 24.40% in BRISQUE, while also achieving the fastest training speed. Additionally, to demonstrate the robustness of Scene4U and allow users to experience immersive scenes from various landmarks, we build WorldVista3D dataset for 3D scene reconstruction, which contains panoramic images of globally renowned sites. The implementation code and dataset will be released at https://github.com/LongHZ140516/Scene4U .

• The paper introduces Scene4U, a framework that reconstructs hierarchical, layered 3D scenes from a single panoramic image by decomposing it into semantic layers, repairing occlusions, and optimizing a 3D Gaussian Splatting representation.
• Quantitative evaluations demonstrate that Scene4U achieves significant improvements in visual quality metrics (LPIPS, BRISQUE) and faster training times compared to existing state-of-the-art 3D reconstruction methods.
• Scene4U has practical implications for virtual and augmented reality applications and is validated on the diverse WorldVista3D dataset, showcasing its potential to advance immersive exploration by addressing challenges like dynamic occlusions and textural discontinuities.

Overview of "Scene4U: Hierarchical Layered 3D Scene Reconstruction from a Single Panoramic Image"

The paper introduces Scene4U, a novel framework designed to reconstruct hierarchical 3D scenes from a single panoramic image, advancing the field of 3D scene representation and immersive virtual exploration. This method utilizes a unique layered reconstruction approach to produce high-fidelity, globally consistent 3D scenes, free from dynamic obstructions like pedestrians and vehicles.

In current state-of-the-art image-driven 3D reconstruction techniques, the challenge of maintaining global texture consistency while allowing for unrestricted exploration persists. Conventional methods often suffer from visual discontinuities across different camera views and exhibit voids due to occlusion by dynamic foreground objects. Scene4U responds to these challenges with a framework that integrates open-vocabulary segmentation and LLMs to dissect the panorama into multiple semantic layers. This facilitates a robust hierarchy that integrates a diffusion model-based layering repair module, enhancing the scene's depth and visual coherence.

Methodology

Scene4U operates through several distinct phases:

1. Scene Layer Decomposition: By employing an open-vocabulary segmentation model, the input panoramic image is segmented into distinct semantic layers. This is enhanced by LLMs that improve the classification of foreground and background regions, crucial for handling occlusions.
2. Layered Repair and 3D Initialization: Each segmented layer undergoes a repair process using a diffusion model to address occluded regions, simultaneously integrating depth information. This generates a comprehensive, hierarchical scene representation.
3. 3D Scene Optimization: The scene layers are subsequently transformed into a 3D Gaussian Splatting (3DGS) representation. Optimization follows a hierarchical strategy that refines each layer, ensuring consistency in texture and structural detail throughout the immersive environment.

Results and Implications

The quantitative evaluations of Scene4U reveal a substantial improvement over existing methods, with enhancements of 24.24% in LPIPS and 24.40% in BRISQUE metrics. The framework achieves these results while maintaining the most rapid training time among tested methods. This advancement indicates not only practical applications in virtual reality and augmented reality fields but also pushes the theoretical boundaries of 3D scene reconstruction technologies.

The introduction of the multi-faceted WorldVista3D dataset further highlights the framework's capability by providing a testing ground for diverse landmark panorama images, underscoring the system's versatility and robustness across varied global contexts.

Future Directions

Looking forward, Scene4U's integration of panoramic imagery and multi-layer segmentation with machine learning models could inform broader AI advancements in multi-view stereo and real-time 3D environment interactions. Further research could enhance model training efficiency and application scalability, as well as explore deeper integrations of artificial intelligence in reconstructive and generative tasks. These improvements would open novel avenues in virtual content creation and interactive storytelling.

Scene4U sets a new standard in panoramic scene synthesis, advancing immersive exploration and addressing the persistent challenges of dynamic occlusions and textural discontinuities. Its layered approach marks a transformative step in refining and unifying the visual and spatial accuracies of virtual experiences.