让3D场景中的文字更清晰可读，提升重建精度。
STRinGS: Selective Text Refinement in Gaussian Splatting
- 分区域优化：先精修文字区，再融合非文字区
- 7000次迭代下文字识别错误率降低63.6%
- 专为含文字场景设计，适合智能导航与虚拟现实
文本作为标识、标签或指令，是真实场景中传递关键上下文信息的重要元素。现有的3D表示方法如3D高斯点阵（3DGS）难以在保持高视觉保真度的同时保留细粒度文本细节，微小的文本重建误差会导致显著语义丢失。本文提出STRinGS——一种面向文本的有选择性精修框架，对文本与非文本区域分别处理：优先精修文本区域，随后与非文本区域合并进行全场景优化。该方法能在复杂配置下生成清晰可读的文本。我们引入基于OCR字符错误率（CER）的可读性评估指标，并在新构建的多场景数据集STRinGS-360上验证效果。STRinGS在仅7000次迭代下相较原始3DGS实现63.6%的相对性能提升，推动了文本丰富环境下的3D场景理解边界，为更鲁棒的文本感知重建方法奠定基础。
原文摘要 · Abstract (English)
Text as signs, labels, or instructions is a critical element of real-world scenes as they can convey important contextual information. 3D representations such as 3D Gaussian Splatting (3DGS) struggle to preserve fine-grained text details, while achieving high visual fidelity. Small errors in textual element reconstruction can lead to significant semantic loss. We propose STRinGS, a text-aware, selective refinement framework to address this issue for 3DGS reconstruction. Our method treats text and non-text regions separately, refining text regions first and merging them with non-text regions later for full-scene optimization. STRinGS produces sharp, readable text even in challenging configurations. We introduce a text readability measure OCR Character Error Rate (CER) to evaluate the efficacy on text regions. STRinGS results in a 63.6% relative improvement over 3DGS at just 7K iterations. We also introduce a curated dataset STRinGS-360 with diverse text scenarios to evaluate text readability in 3D reconstruction. Our method and dataset together push the boundaries of 3D scene understanding in text-rich environments, paving the way for more robust text-aware reconstruction methods.
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