We propose a novel framework for RGB-based category-level 6D object pose and
size estimation. Our approach relies on the prediction of normalized object
coordinate space (NOCS), which serves as an efficient and effective object
canonical representation that can be extracted from RGB images. Unlike previous
approaches that heavily relied on additional depth readings as input, our
novelty lies in leveraging multi-view information, which is commonly available
in practical scenarios where a moving camera continuously observes the
environment. By introducing multi-view constraints, we can obtain accurate
camera pose and depth estimation from a monocular dense SLAM framework.
Additionally, by incorporating constraints on the camera relative pose, we can
apply trimming strategies and robust pose averaging on the multi-view object
poses, resulting in more accurate and robust estimations of category-level
object poses even in the absence of direct depth readings. Furthermore, we
introduce a novel NOCS prediction network that significantly improves
performance. Our experimental results demonstrate the strong performance of our
proposed method, even comparable to state-of-the-art RGB-D methods across
public dataset sequences. Additionally, we showcase the generalization ability
of our method by evaluating it on self-collected datasets