We introduce Diff-DOPE, a 6-DoF pose refiner that takes as input an image, a
3D textured model of an object, and an initial pose of the object. The method
uses differentiable rendering to update the object pose to minimize the visual
error between the image and the projection of the model. We show that this
simple, yet effective, idea is able to achieve state-of-the-art results on pose
estimation datasets. Our approach is a departure from recent methods in which
the pose refiner is a deep neural network trained on a large synthetic dataset
to map inputs to refinement steps. Rather, our use of differentiable rendering
allows us to avoid training altogether. Our approach performs multiple gradient
descent optimizations in parallel with different random learning rates to avoid
local minima from symmetric objects, similar appearances, or wrong step size.
Various modalities can be used, e.g., RGB, depth, intensity edges, and object
segmentation masks. We present experiments examining the effect of various
choices, showing that the best results are found when the RGB image is
accompanied by an object mask and depth image to guide the optimization
process.

我们引入了 Diff-DOPE，这是一种 6 自由度姿态微调器，其输入为图像、一个物体的 3D 纹理模型和物体的初始姿态。这种方法使用可微渲染来更新物体姿态，以减小图像与模型投影之间的视觉误差，我们展示了这种简单且有效的想法能在姿态估计数据集上达到最先进的结果。我们的方法与最近的方法有所不同，最近的方法中姿态微调器是在大型合成数据集上训练的深度神经网络，用于将输入映射到微调步骤，而我们的可微渲染的使用使我们能够完全避免训练。我们的方法可以同时进行多个梯度下降优化，使用不同的随机学习率，以避免对称对象、相似外观或错误步长的局部最小值。可以使用各种模态，例如 RGB、深度、强度边缘和物体分割掩码。我们进行了一系列实验，研究了各种选项的效果，结果表明，当 RGB 图像与物体掩码和深度图像一起用于引导优化过程时，能获得最佳的结果。