从轮廓图精准求解3D物体全局最优姿态，无需对应点。
Globally Optimal Pose from Orthographic Silhouettes
- 利用轮廓面积在旋转空间的连续性，构建预计算的轮廓签名响应面。
- 结合椭圆拟合的宽高比辅助特征，实现高效搜索并获得全局最优解。
- 适用于任意形状（凸/非凸、不同亏格），无须点对应，适合三维重建场景。
本文解决从无遮挡轮廓图中确定已知三维形状姿态的问题。通过利用轮廓面积在旋转空间中的连续性这一简单但未被充分探索的性质，提出一种基于预计算轮廓签名响应面的方法。查询该响应面可大幅缩小旋转搜索空间，使基于分辨率引导的候选搜索成为可能。此外，引入拟合轮廓得到的二维椭圆宽高比作为辅助全局形状签名，进一步加速姿态搜索。该方法是首个无需依赖对应关系、适用于任意形状（无论凸性或亏格）的高效全局最优姿态估计方法。在合成与真实数据上验证，精度显著优于现有方法。代码、数据及补充材料见：https://agnivsen.github.io/pose-from-silhouette/
原文摘要 · Abstract (English)
We solve the problem of determining the pose of known shapes in $\mathbb{R}^3$ from their unoccluded silhouettes. The pose is determined up to global optimality using a simple yet under-explored property of the area-of-silhouette: its continuity w.r.t trajectories in the rotation space. The proposed method utilises pre-computed silhouette-signatures, modelled as a response surface of the area-of-silhouettes. Querying this silhouette-signature response surface for pose estimation leads to a strong branching of the rotation search space, making resolution-guided candidate search feasible. Additionally, we utilise the aspect ratio of 2D ellipses fitted to projected silhouettes as an auxiliary global shape signature to accelerate the pose search. This combined strategy forms the first method to efficiently estimate globally optimal pose from just the silhouettes, without being guided by correspondences, for any shape, irrespective of its convexity and genus. We validate our method on synthetic and real examples, demonstrating significantly improved accuracy against comparable approaches. Code, data, and supplementary in: https://agnivsen.github.io/pose-from-silhouette/
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