  This paper studies category-level object pose estimation based on a single
monocular image. Recent advances in pose-aware generative models have paved the
way for addressing this challenging task using analysis-by-synthesis. The idea
is to sequentially update a set of latent variables, e.g., pose, shape, and
appearance, of the generative model until the generated image best agrees with
the observation. However, convergence and efficiency are two challenges of this
inference procedure. In this paper, we take a deeper look at the inference of
analysis-by-synthesis from the perspective of visual navigation, and
investigate what is a good navigation policy for this specific task. We
evaluate three different strategies, including gradient descent, reinforcement
learning and imitation learning, via thorough comparisons in terms of
convergence, robustness and efficiency. Moreover, we show that a simple hybrid
approach leads to an effective and efficient solution. We further compare these
strategies to state-of-the-art methods, and demonstrate superior performance on
synthetic and real-world datasets leveraging off-the-shelf pose-aware
generative models.
