用重投影误差引导选择有效区域，提升场景坐标回归精度。
Reprojection Errors as Prompts for Efficient Scene Coordinate Regression
- 基于重投影误差筛选图像区域，避免无效区域干扰训练
- 结合SAM生成误差引导掩码，迭代优化点采样策略
- 在Cambridge Landmarks和Indoor6上超越无3D信息的现有方法
场景坐标回归（SCR）因其在视觉定位中的高精度潜力成为研究热点。然而，现有方法通常对图像所有区域进行训练，包括动态物体和纹理缺失区域，这些区域会损害模型性能与效率。本文通过深入分析验证了此类区域的负面影响，并提出一种误差引导特征选择（EGFS）机制，结合分割任意模型（SAM），将低重投影区域作为提示，扩展为误差引导掩码，进而迭代采样并剔除问题区域。实验表明，该方法在Cambridge Landmarks和Indoor6数据集上优于不依赖3D信息的现有SCR方法。
原文摘要 · Abstract (English)
Scene coordinate regression (SCR) methods have emerged as a promising area of research due to their potential for accurate visual localization. However, many existing SCR approaches train on samples from all image regions, including dynamic objects and texture-less areas. Utilizing these areas for optimization during training can potentially hamper the overall performance and efficiency of the model. In this study, we first perform an in-depth analysis to validate the adverse impacts of these areas. Drawing inspiration from our analysis, we then introduce an error-guided feature selection (EGFS) mechanism, in tandem with the use of the Segment Anything Model (SAM). This mechanism seeds low reprojection areas as prompts and expands them into error-guided masks, and then utilizes these masks to sample points and filter out problematic areas in an iterative manner. The experiments demonstrate that our method outperforms existing SCR approaches that do not rely on 3D information on the Cambridge Landmarks and Indoor6 datasets.
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