Iterative Optimisation with an Innovation CNN for Pose Refinement
This addresses the problem of accurate pose estimation for robotics or AR/VR applications without high-quality 3D models, representing a novel method for a known bottleneck.
The paper tackles object pose estimation from a single RGB image by proposing an Innovation CNN for iterative refinement without requiring textured 3D models, achieving state-of-the-art performance on LINEMOD and Occlusion LINEMOD datasets.
Object pose estimation from a single RGB image is a challenging problem due to variable lighting conditions and viewpoint changes. The most accurate pose estimation networks implement pose refinement via reprojection of a known, textured 3D model, however, such methods cannot be applied without high quality 3D models of the observed objects. In this work we propose an approach, namely an Innovation CNN, to object pose estimation refinement that overcomes the requirement for reprojecting a textured 3D model. Our approach improves initial pose estimation progressively by applying the Innovation CNN iteratively in a stochastic gradient descent (SGD) framework. We evaluate our method on the popular LINEMOD and Occlusion LINEMOD datasets and obtain state-of-the-art performance on both datasets.