A Novel Convolution and Attention Mechanism-based Model for 6D Object Pose Estimation
This addresses the challenge of estimating 3D poses from 2D images for robotics and AR/VR applications, representing an incremental improvement over existing methods.
The paper tackles the problem of 6D object pose estimation from RGB images by introducing a graph-based representation with spatial attention and Legendre convolution, achieving state-of-the-art results on LINEMOD, Occluded LINEMOD, and YCB Video datasets.
Estimating 6D object poses from RGB images is challenging because the lack of depth information requires inferring a three dimensional structure from 2D projections. Traditional methods often rely on deep learning with grid based data structures but struggle to capture complex dependencies among extracted features. To overcome this, we introduce a graph based representation derived directly from images, where spatial temporal features of each pixel serve as nodes, and relationships between them are defined through node connectivity and spatial interactions. We also employ feature selection mechanisms that use spatial attention and self attention distillation, along with a Legendre convolution layer leveraging the orthogonality of Legendre polynomials for numerical stability. Experiments on the LINEMOD, Occluded LINEMOD, and YCB Video datasets demonstrate that our method outperforms nine existing approaches and achieves state of the art benchmark in object pose estimation.