STRinGS: Selective Text Refinement in Gaussian Splatting

• URL: http://arxiv.org/abs/2512.07230v1
• Date: Mon, 08 Dec 2025 07:20:01 GMT
• Title: STRinGS: Selective Text Refinement in Gaussian Splatting
• Abstract summary: 3D representations such as 3D Gaussian Splatting struggle to preserve fine-grained text details, while achieving high visual fidelity.<n>We propose STRinGS, a text-aware, selective refinement framework to address this issue for 3DGS reconstruction.<n>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.
• Score: 25.210268885595983
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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