论文标题
稳健姿势估计的神经辐射场的平行反转
Parallel Inversion of Neural Radiance Fields for Robust Pose Estimation
论文作者
论文摘要
我们提出了一种基于快速神经辐射场（NERF）的平行优化方法，用于估计相机相对于对象或场景的6多种姿势。给定一个观察到的目标的RGB图像，我们可以通过最大程度地减少从快速NERF模型和观察到的图像中的像素呈现的像素之间的残差来预测相机的翻译和旋转。我们将基于动量的相机外部优化过程集成到即时神经图形原始图中，这是最近非常快速的NERF实现。通过将平行的蒙特卡洛采样引入姿势估计任务中，我们的方法克服了本地的最小值并提高了更广泛的搜索空间的效率。我们还表明了采用更强大的基于像素的损失功能以减少错误的重要性。实验表明，我们的方法可以改善合成和现实基准的概括和鲁棒性。
We present a parallelized optimization method based on fast Neural Radiance Fields (NeRF) for estimating 6-DoF pose of a camera with respect to an object or scene. Given a single observed RGB image of the target, we can predict the translation and rotation of the camera by minimizing the residual between pixels rendered from a fast NeRF model and pixels in the observed image. We integrate a momentum-based camera extrinsic optimization procedure into Instant Neural Graphics Primitives, a recent exceptionally fast NeRF implementation. By introducing parallel Monte Carlo sampling into the pose estimation task, our method overcomes local minima and improves efficiency in a more extensive search space. We also show the importance of adopting a more robust pixel-based loss function to reduce error. Experiments demonstrate that our method can achieve improved generalization and robustness on both synthetic and real-world benchmarks.
