LayerPano3D: Layered 3D Panorama for Hyper-Immersive Scene Generation (2408.13252v1)

Abstract: 3D immersive scene generation is a challenging yet critical task in computer vision and graphics. A desired virtual 3D scene should 1) exhibit omnidirectional view consistency, and 2) allow for free exploration in complex scene hierarchies. Existing methods either rely on successive scene expansion via inpainting or employ panorama representation to represent large FOV scene environments. However, the generated scene suffers from semantic drift during expansion and is unable to handle occlusion among scene hierarchies. To tackle these challenges, we introduce LayerPano3D, a novel framework for full-view, explorable panoramic 3D scene generation from a single text prompt. Our key insight is to decompose a reference 2D panorama into multiple layers at different depth levels, where each layer reveals the unseen space from the reference views via diffusion prior. LayerPano3D comprises multiple dedicated designs: 1) we introduce a novel text-guided anchor view synthesis pipeline for high-quality, consistent panorama generation. 2) We pioneer the Layered 3D Panorama as underlying representation to manage complex scene hierarchies and lift it into 3D Gaussians to splat detailed 360-degree omnidirectional scenes with unconstrained viewing paths. Extensive experiments demonstrate that our framework generates state-of-the-art 3D panoramic scene in both full view consistency and immersive exploratory experience. We believe that LayerPano3D holds promise for advancing 3D panoramic scene creation with numerous applications.

LayerPano3D: Layered 3D Panorama for Hyper-Immersive Scene Generation

Abstract

The paper introduces "LayerPano3D", a novel framework designed to tackle the challenges of text-driven 3D immersive scene generation by leveraging a layered 3D panorama approach. The research identifies key requirements for an ideal virtual 3D scene, primarily omnidirectional view consistency and freedom for exploration within complex scene hierarchies. Existing methods face challenges such as semantic drift and occlusion management. LayerPano3D addresses these by decomposing a reference 2D panorama into multiple depth layers, employing diffusion priors, and representing the 3D scene using 3D Gaussians. The framework's three-fold contribution includes a novel text-guided anchor view synthesis pipeline, the layered 3D panorama representation for handling scene hierarchies, and the capability for hyper-immersive, explorable panoramic scene generation. The extensive experiments validate its state-of-the-art performance in generating high-quality, coherent 3D panoramic scenes.

Introduction

The advancement in spatial computing technologies, including VR and MR, necessitates the creation of high-quality, explorable 3D environments. Traditional scene generation methods produce inconsistent results, especially noticeable in large-scale panoramic images due to issues like semantic drift and occlusion management. The paper proposes LayerPano3D, a solution to these challenges, by leveraging a multi-layered 3D panoramic approach that ensures high image quality and supports intricate scene exploration paths.

LayerPano3D comprises three stages:

1. Text-Guided Anchor View Synthesis: Produces high-quality, consistent panoramic base images.
2. Layered 3D Panorama Construction: Decomposes the panorama into multiple depth layers to manage scene complexity and handle occlusions.
3. 3D Gaussian Scene Optimization: Transforms layered 3D panorama into 3D Gaussians, facilitating free exploration within the generated scene.

Method

The method is meticulously designed in three stages to ensure robust scene generation and exploration capabilities.

Stage I: Reference Panorama Generation

The process begins by generating four orthogonal anchor views using a fine-tuned diffusion model based on Stable Diffusion XL (SDXL). These anchor views are processed to eliminate inconsistencies and synthesized into a high-quality, consistent panorama. The sequence starts by projecting the anchor views into a panorama covering a $360^\circ \times 60^\circ$ field of view, and subsequently expanding it incrementally to cover the full $360^\circ \times 180^\circ$ view using a circular blending strategy to ensure seamless boundary integration.

Stage II: Multi-Layer Panorama Construction

The generated reference panorama is decomposed into multiple depth layers, representing different depth levels and ensuring comprehensive scene coverage. This stage employs panoptic segmentation to identify and cluster assets by depth, filling in occluded regions layer by layer using an enhanced version of PanFusion adapted for panoramic inpainting. Each completed layer is aligned in a shared space, with a resolution enhancement step to ensure high-quality texture representation, especially for distant layers.

Stage III: Panoramic 3D Gaussian Scene Optimization

In the final stage, the layered panoramic images are transformed into 3D Gaussian representations, a technique that supports efficient scene optimization and rendering. The process includes noise filtering to eliminate outliers from the point cloud data, iterative Gaussian training for optimizing scene layers, and a Gaussian selector module that re-activates and optimizes occluding Gaussians to resolve conflicts between layers. This enables the creation of a seamless, navigable 3D environment.

Experiments and Results

The paper details extensive qualitative and quantitative comparisons to validate the efficacy of LayerPano3D:

• Qualitative Comparisons: Demonstrations include various scenarios of panoramic scene generation, highlighting the superior quality, resolution, and consistency brought by LayerPano3D over other state-of-the-art methods.
• Quantitative Comparisons: Metrics such as FID, CLIP, NIQE, and SSIM were used, where LayerPano3D consistently outperformed alternative techniques. Additionally, user studies reinforced the preference for LayerPano3D's outputs based on coherence, plausibility, and compatibility with prompts.
• Ablation Studies: These studies underscored the impact of specific design choices like the circular blending strategy for panorama synthesis and the Gaussian selector's role in mitigating depth alignment issues.

Conclusion

LayerPano3D emerges as a robust framework for generating high-quality, explorable 3D panoramic scenes from textual inputs. The innovative combination of text-guided anchor view synthesis, layered scene decomposition, and 3D Gaussian optimization addresses key challenges in scene generation, offering a significant improvement in both visual fidelity and navigational freedom. Future work could build upon this framework by exploring more sophisticated depth estimation techniques to further enhance scene geometry and realism.

Implications and Future Directions

The practical implications of this research extend to various domains within AI and digital content creation. By improving the quality and flexibility of 3D scene generation, LayerPano3D opens new possibilities for virtual reality, gaming, and immersive simulations. Theoretically, the multi-layered approach to panoramic scene decomposition could inspire further research into more complex scene representations and depth estimation techniques. Future developments might refine the Gaussian optimization methods or integrate additional sensory inputs to enrich the immersive experience further.