2 research outputs found
Accurate 6D Object Pose Estimation by Pose Conditioned Mesh Reconstruction
Current 6D object pose methods consist of deep CNN models fully optimized for
a single object but with its architecture standardized among objects with
different shapes. In contrast to previous works, we explicitly exploit each
object's distinct topological information i.e. 3D dense meshes in the pose
estimation model, with an automated process and prior to any post-processing
refinement stage. In order to achieve this, we propose a learning framework in
which a Graph Convolutional Neural Network reconstructs a pose conditioned 3D
mesh of the object. A robust estimation of the allocentric orientation is
recovered by computing, in a differentiable manner, the Procrustes' alignment
between the canonical and reconstructed dense 3D meshes. 6D egocentric pose is
then lifted using additional mask and 2D centroid projection estimations. Our
method is capable of self validating its pose estimation by measuring the
quality of the reconstructed mesh, which is invaluable in real life
applications. In our experiments on the LINEMOD, OCCLUSION and YCB-Video
benchmarks, the proposed method outperforms state-of-the-arts