We present RePOSE, a fast iterative refinement method for 6D object pose
estimation. Prior methods perform refinement by feeding zoomed-in input and
rendered RGB images into a CNN and directly regressing an update of a refined
pose. Their runtime is slow due to the computational cost of CNN, which is
especially prominent in multiple-object pose refinement. To overcome this
problem, RePOSE leverages image rendering for fast feature extraction using a
3D model with a learnable texture. We call this deep texture rendering, which
uses a shallow multi-layer perceptron to directly regress a view-invariant
image representation of an object. Furthermore, we utilize differentiable
Levenberg-Marquardt (LM) optimization to refine a pose fast and accurately by
minimizing the feature-metric error between the input and rendered image
representations without the need of zooming in. These image representations are
trained such that differentiable LM optimization converges within few
iterations. Consequently, RePOSE runs at 92 FPS and achieves state-of-the-art
accuracy of 51.6% on the Occlusion LineMOD dataset - a 4.1% absolute
improvement over the prior art, and comparable result on the YCB-Video dataset
with a much faster runtime. The code is available at
this https URL

RePOSE 是一种快速的迭代精确定位 6D 物体姿态的方法，通过深浅层感知器的直接回归对象视觉无关图像表示，差分 LM 优化迭代得到姿态的最小特征度量误差，从而实现比现有技术更高的准确性，适用于要求计算速度的实时物体姿态估计。