LayerPano3D: Layered 3D Panorama for Hyper-Immersive Scene Generation
Shuai Yang, Jing Tan, Mengchen Zhang, Tong Wu, Yixuan Li, Gordon, Wetzstein, Ziwei Liu, Dahua Lin

TL;DR
Layerpano3D is a novel framework that generates full-view, explorable 3D panoramic scenes from a single text prompt by decomposing panoramas into layered depth levels and using diffusion models.
Contribution
It introduces a layered 3D panorama representation, a new upright panorama dataset, and a diffusion-based method for high-quality, consistent 3D scene generation from text.
Findings
Achieves state-of-the-art full view consistency
Enables immersive exploration of generated scenes
Demonstrates high-quality panoramic scene synthesis
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…
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Taxonomy
Topics3D Surveying and Cultural Heritage · Advanced Vision and Imaging · Computer Graphics and Visualization Techniques
MethodsDiffusion · Inpainting
