用重力对齐坐标系提升点云处理精度，让3D重建更稳定。
G3T Up! Gravity Aligned Coordinate Frames Simplify Pointmap Processing
- 将点云从相机坐标转为重力对齐坐标，减少旋转自由度
- 新模型G3T在真实场景中实现高精度的竖直点云与相机位姿预测
- 适用于需要稳定三维重建的自动驾驶、机器人领域
当前基于前馈的3D重建方法（如VGGT）在以相机为中心的坐标系中预测像素对齐的点云图。然而，这种坐标系并非总是最优。本文提出在重力对齐的竖直坐标系中预测点云图，利用现实场景中普遍存在的结构线索。与相机坐标系不同，重力对齐坐标系在多视角间共享同一垂直轴，显著降低点云间关联所需的旋转自由度。为此，我们引入重力锚定几何变换器（G3T），在重力对齐的3D数据上微调现有模型。G3T可生成高度准确的重力感知预测，包括竖直点云和相机到重力的位姿。此外，我们提出G3T-Long，一种基于子图的增量式3D重建流程，充分利用竖直坐标系带来的旋转自由度减少，显著提升重建精度。
原文摘要 · Abstract (English)
Modern feed-forward 3D reconstruction methods like VGGT predict pixel-aligned pointmaps in camera-centric coordinate frames. However, this choice of coordinate frame is not always optimal. We propose instead to predict pointmaps in upright, gravity-aligned frames that exploit strong structural cues present in many real-world scenes. Unlike camera-centric frames, gravity-aligned frames share a common vertical axis across viewpoints, reducing the rotational degrees of freedom needed to relate pointmaps to one another. To this end, we introduce the Gravity Grounded Geometry Transformer (G3T), fine-tuned from existing models on gravity-aligned 3D data. G3T produces highly accurate gravity-aware predictions, including upright pointmaps and camera-to-gravity poses. We further introduce G3T-Long, a submap-based incremental 3D reconstruction pipeline that leverages the reduced rotational degrees of freedom afforded by upright frames to achieve significantly improved reconstruction accuracy.
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