用智能选位提升3D高斯点云的合成视角质量，加速定位模型训练。
PoseCompass: Intelligent Synthetic Pose Selection for Visual Localization
- 基于难度、覆盖度和可观察性三维度筛选高价值合成视角。
- 在7-Scenes上将适配时间缩短至5.1分钟，误差降低53.8%。
- 适合需要高效数据增强的视觉定位研究者使用。
在视觉定位中，绝对位姿回归（APR）可实现单图实时6自由度相机位姿推断，但严重依赖微调数据的质量与覆盖范围。现有方法利用3D高斯点云（3DGS）进行新视角合成以增强数据，但随机采样会产生冗余视角及低质量区域的噪声样本。为此，本文提出PoseCompass，一种面向3DGS的智能位姿选择流程。该方法构建价值驱动的位姿排序机制，综合三个维度：定位难度（偏好困难区域）、覆盖新颖性（探索未充分采样区域）、渲染可观测性（过滤伪影与噪声）。随后生成轨迹约束候选位姿，选取前K个高分位姿，通过轻量级扩散对齐的3DGS合成视图，并在真实与合成数据混合集上微调位姿回归器。在7-Scenes数据集上，该方法将适配时间从15.2分钟降至5.1分钟（提速3倍），中位姿态误差减少53.8%，显著优于随机基线。
原文摘要 · Abstract (English)
In visual localization, Absolute Pose Regression (APR) enables real-time 6-DoF camera pose inference from single images, yet critically depends on fine-tuning data quality and coverage. While recent methods leverage 3D Gaussian Splatting (3DGS) for novel view synthesis-based data augmentation, random sampling generates redundant views and noisy samples from poorly reconstructed regions. To mitigate this research gap, we propose PoseCompass, an intelligent pose selection pipeline for 3DGS-based APR. PoseCompass formulates synthetic pose selection and derives a value-based pose ranking mechanism to identify informative poses. The ranking integrates three dimensions: Localization Difficulty, favoring challenging regions; Coverage Novelty, exploring under-sampled areas; and Rendering Observability, filtering artifacts and noise. PoseCompass then generates trajectory-constrained candidates, selects the top-K ranked poses, and synthesizes views using 3DGS with lightweight diffusion-based alignment. Finally, the pose regressor is fine-tuned on mixed real and synthetic data. We evaluate PoseCompass on 7-Scenes, where it reduces adaptation time from 15.2 to 5.1 minutes, a 3x speedup, while cutting median pose errors by 53.8 percent and significantly outperforming random baselines.
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