PSGS：通过高斯点划法实现文本驱动的全景滑动场景生成
PSGS: Text-driven Panorama Sliding Scene Generation via Gaussian Splatting
AI总结：
PSGS通过高斯点划法和双阶段框架实现高质量文本驱动全景场景生成，提升3D场景的真实感和细节保真度。
摘要：
生成逼真的3D场景对于沉浸式应用如VR、AR和游戏至关重要。虽然文本驱动的方法承诺了效率，但现有方法在有限的3D文本数据和不一致的多视角拼接方面存在不足，导致生成的场景过于简单。为此，我们提出了PSGS，一种两阶段框架，用于高质量的全景场景生成。首先，一种新的双层优化架构生成语义连贯的全景图：布局推理层将文本解析为结构化的空间关系，而自我优化层通过迭代MLLM反馈来细化视觉细节。其次，我们的全景滑动机制通过战略性采样重叠视角来初始化全局一致的3D高斯点划法点云。通过在训练过程中结合深度和语义连贯性损失，我们大大提高了渲染场景的质量和细节保真度。我们的实验表明，PSGS在全景生成方面优于现有方法，并能生成更具吸引力的3D场景，为可扩展的沉浸式内容创作提供了一种稳健的解决方案。
英文摘要：
Generating realistic 3D scenes from text is crucial for immersive applications like VR, AR, and gaming. While text-driven approaches promise efficiency, existing methods suffer from limited 3D-text data and inconsistent multi-view stitching, resulting in overly simplistic scenes. To address this, we propose PSGS, a two-stage framework for high-fidelity panoramic scene generation. First, a novel two-layer optimization architecture generates semantically coherent panoramas: a layout reasoning layer parses text into structured spatial relationships, while a self-optimization layer refines visual details via iterative MLLM feedback. Second, our panorama sliding mechanism initializes globally consistent 3D Gaussian Splatting point clouds by strategically sampling overlapping perspectives. By incorporating depth and semantic coherence losses during training, we greatly improve the quality and detail fidelity of rendered scenes. Our experiments demonstrate that PSGS outperforms existing methods in panorama generation and produces more appealing 3D scenes, offering a robust solution for scalable immersive content creation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
