聚焦兴趣物体，提升细节质量并缩小模型体积
ROI-GS: Interest-based Local Quality 3D Gaussian Splatting
- 根据目标物体选择相机并针对性训练，优化局部细节
- 局部质量最高提升2.96 dB PSNR，模型尺寸减少约17%
- 适合需要高精度物体重建的实时3D场景应用
针对高精度重建兴趣物体的挑战，现有3D高斯泼溅（3DGS）方法在全场景均匀分配资源，导致兴趣区域细节受限且模型膨胀。本文提出ROI-GS，一种面向物体的框架，通过物体引导的相机选择、目标物体专属训练，以及将高保真物体重建无缝融合至全局场景，实现对选定物体的高分辨率细节增强，同时保持实时性能。实验表明，该方法显著提升局部质量（最高达2.96 dB PSNR），整体模型大小减少约17%，单物体场景下训练速度更快，优于现有方法。
原文摘要 · Abstract (English)
We tackle the challenge of efficiently reconstructing 3D scenes with high detail on objects of interest. Existing 3D Gaussian Splatting (3DGS) methods allocate resources uniformly across the scene, limiting fine detail to Regions Of Interest (ROIs) and leading to inflated model size. We propose ROI-GS, an object-aware framework that enhances local details through object-guided camera selection, targeted Object training, and seamless integration of high-fidelity object of interest reconstructions into the global scene. Our method prioritizes higher resolution details on chosen objects while maintaining real-time performance. Experiments show that ROI-GS significantly improves local quality (up to 2.96 dB PSNR), while reducing overall model size by $\approx 17\%$ of baseline and achieving faster training for a scene with a single object of interest, outperforming existing methods.
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