BloomScene：用于跨模态场景生成的轻量级结构化3D高斯泼溅
BloomScene: Lightweight Structured 3D Gaussian Splatting for Crossmodal Scene Generation
AI总结：
针对现有3D场景生成方法存储占用高、易出现几何畸变的问题，提出轻量级结构化3D高斯泼溅框架BloomScene，通过跨模态渐进生成、分层深度正则化和结构化压缩，实现文本/图像输入的高质量低开销3D场景生成。
摘要：
随着虚拟现实应用的广泛普及，3D场景生成已成为一项极具挑战性的全新研究前沿。3D场景结构高度复杂，需确保输出结果稠密、连贯且包含所有必要结构。当前许多3D场景生成方法依赖预训练的文本到图像扩散模型和单目深度估计器，但生成的场景占用大量存储空间，且往往缺乏有效的正则化方法，导致几何畸变。为此，我们提出BloomScene——一种用于跨模态场景生成的轻量级结构化3D高斯泼溅方法，可从文本或图像输入生成多样化、高质量的3D场景。具体而言，我们提出了跨模态渐进式场景生成框架，利用增量点云重建与3D高斯泼溅生成连贯场景。此外，我们提出了基于分层深度先验的正则化机制，通过对深度精度和平滑度的多级约束，提升生成场景的真实感与连续性。最终，我们提出了结构化上下文引导的压缩机制，利用结构化哈希网格对无组织锚点属性的上下文进行建模，可显著消除结构冗余并降低存储开销。在多个场景上开展的综合实验表明，与若干基线方法相比，我们的框架具备显著的潜力与优势。
英文摘要：
With the widespread use of virtual reality applications, 3D scene generation has become a new challenging research frontier. 3D scenes have highly complex structures and need to ensure that the output is dense, coherent, and contains all necessary structures. Many current 3D scene generation methods rely on pre-trained text-to-image diffusion models and monocular depth estimators. However, the generated scenes occupy large amounts of storage space and often lack effective regularisation methods, leading to geometric distortions. To this end, we propose BloomScene, a lightweight structured 3D Gaussian splatting for crossmodal scene generation, which creates diverse and high-quality 3D scenes from text or image inputs. Specifically, a crossmodal progressive scene generation framework is proposed to generate coherent scenes utilizing incremental point cloud reconstruction and 3D Gaussian splatting. Additionally, we propose a hierarchical depth prior-based regularization mechanism that utilizes multi-level constraints on depth accuracy and smoothness to enhance the realism and continuity of the generated scenes. Ultimately, we propose a structured context-guided compression mechanism that exploits structured hash grids to model the context of unorganized anchor attributes, which significantly eliminates structural redundancy and reduces storage overhead. Comprehensive experiments across multiple scenes demonstrate the significant potential and advantages of our framework compared with several baselines.
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。
