从多视角轮廓图中稳健估计不规则物体的旋转轴，无需精确定位中心。
PoleStack: Robust Pole Estimation of Irregular Objects from Silhouette Stacking
- 通过轮廓堆叠生成对称性特征，利用傅里叶变换实现平移不变性。
- 在低分辨率图像下达到度级精度，抗阴影和定位误差干扰。
- 适用于靠近目标或悬停时的实时旋转轴估计，适合空间任务。
本文提出一种算法，通过从多个相机姿态采集的轮廓图像估计主轴旋转体的旋转极。首先，将一组图像堆叠形成单个轮廓堆叠图，物体旋转会在投影极方向上引入反射对称性。通过检测轮廓堆叠中的最大对称性来估计该投影极方向。为应对质心位置未知的问题，采用离散傅里叶变换生成轮廓堆叠的幅度谱，实现平移不变性并增强对噪声的鲁棒性。其次，通过融合不同相机视角下的两个或以上投影极测量值，估计三维极方向。实验表明，在低分辨率图像下可实现度级精度，对严重表面阴影和基于质心的图像配准误差具有强鲁棒性。该方法适用于目标接近阶段及悬停状态下的极点估计。
原文摘要 · Abstract (English)
We present an algorithm to estimate the rotation pole of a principal-axis rotator using silhouette images collected from multiple camera poses. First, a set of images is stacked to form a single silhouette-stack image, where the object's rotation introduces reflective symmetry about the imaged pole direction. We estimate this projected-pole direction by identifying maximum symmetry in the silhouette stack. To handle unknown center-of-mass image location, we apply the Discrete Fourier Transform to produce the silhouette-stack amplitude spectrum, achieving translation invariance and increased robustness to noise. Second, the 3D pole orientation is estimated by combining two or more projected-pole measurements collected from different camera orientations. We demonstrate degree-level pole estimation accuracy using low-resolution imagery, showing robustness to severe surface shadowing and centroid-based image-registration errors. The proposed approach could be suitable for pole estimation during both the approach phase toward a target object and while hovering.
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