论文标题
距离操作中姿势估计的可区分渲染
Differentiable Rendering for Pose Estimation in Proximity Operations
论文作者
论文摘要
可区分的渲染旨在计算相对于渲染参数的图像渲染函数的导数。本文提出了一种新型算法，用于使用可区分的渲染管道，通过基于梯度的优化进行6DOF姿势估计。我们强调了两个关键的贡献：（1）仅通过稀疏的2D特征对应关系在2D特征空间中比较图像（使用3D模型渲染）而不是求解常规的2D到3D对应问题和计算再投影误差。 （2）我们通过在线学习计算了渲染过程的近似局部梯度，而不是分析图像形成模型。学习数据包括从姿势社区中的小扰动中提取的图像特征。梯度通过渲染管道传播，以使用非线性最小二乘正方形进行6多种姿势估计。这种基于梯度的优化通过对齐3D模型来重现参考图像形状，直接对姿势参数进行回归。使用代表性实验，我们证明了我们在接近操作中姿势估计的方法的应用。
Differentiable rendering aims to compute the derivative of the image rendering function with respect to the rendering parameters. This paper presents a novel algorithm for 6-DoF pose estimation through gradient-based optimization using a differentiable rendering pipeline. We emphasize two key contributions: (1) instead of solving the conventional 2D to 3D correspondence problem and computing reprojection errors, images (rendered using the 3D model) are compared only in the 2D feature space via sparse 2D feature correspondences. (2) Instead of an analytical image formation model, we compute an approximate local gradient of the rendering process through online learning. The learning data consists of image features extracted from multi-viewpoint renders at small perturbations in the pose neighborhood. The gradients are propagated through the rendering pipeline for the 6-DoF pose estimation using nonlinear least squares. This gradient-based optimization regresses directly upon the pose parameters by aligning the 3D model to reproduce a reference image shape. Using representative experiments, we demonstrate the application of our approach to pose estimation in proximity operations.
