  This paper introduces a novel multi-view 6 DoF object pose refinement
approach focusing on improving methods trained on synthetic data. It is based
on the DPOD detector, which produces dense 2D-3D correspondences between the
model vertices and the image pixels in each frame. We have opted for the use of
multiple frames with known relative camera transformations, as it allows
introduction of geometrical constraints via an interpretable ICP-like loss
function. The loss function is implemented with a differentiable renderer and
is optimized iteratively. We also demonstrate that a full detection and
refinement pipeline, which is trained solely on synthetic data, can be used for
auto-labeling real data. We perform quantitative evaluation on LineMOD,
Occlusion, Homebrewed and YCB-V datasets and report excellent performance in
comparison to the state-of-the-art methods trained on the synthetic and real
data. We demonstrate empirically that our approach requires only a few frames
and is robust to close camera locations and noise in extrinsic camera
calibration, making its practical usage easier and more ubiquitous.

Computer Vision and Pattern Recognition