  Predicting the object's 6D pose from a single RGB image is a fundamental
computer vision task. Generally, the distance between transformed object
vertices is employed as an objective function for pose estimation methods.
However, projective geometry in the camera space is not considered in those
methods and causes performance degradation. In this regard, we propose a new
pose estimation system based on a projective grid instead of object vertices.
Our pose estimation method, dynamic projective spatial transformer network
(DProST), localizes the region of interest grid on the rays in camera space and
transforms the grid to object space by estimated pose. The transformed grid is
used as both a sampling grid and a new criterion of the estimated pose.
Additionally, because DProST does not require object vertices, our method can
be used in a mesh-less setting by replacing the mesh with a reconstructed
feature. Experimental results show that mesh-less DProST outperforms the
state-of-the-art mesh-based methods on the LINEMOD and LINEMOD-OCCLUSION
dataset, and shows competitive performance on the YCBV dataset with mesh data.
The source code is available at https://github.com/parkjaewoo0611/DProST
