3 research outputs found
6DoF Object Pose Estimation via Differentiable Proxy Voting Loss
Estimating a 6DOF object pose from a single image is very challenging due to
occlusions or textureless appearances. Vector-field based keypoint voting has
demonstrated its effectiveness and superiority on tackling those issues.
However, direct regression of vector-fields neglects that the distances between
pixels and keypoints also affect the deviations of hypotheses dramatically. In
other words, small errors in direction vectors may generate severely deviated
hypotheses when pixels are far away from a keypoint. In this paper, we aim to
reduce such errors by incorporating the distances between pixels and keypoints
into our objective. To this end, we develop a simple yet effective
differentiable proxy voting loss (DPVL) which mimics the hypothesis selection
in the voting procedure. By exploiting our voting loss, we are able to train
our network in an end-to-end manner. Experiments on widely used datasets, i.e.,
LINEMOD and Occlusion LINEMOD, manifest that our DPVL improves pose estimation
performance significantly and speeds up the training convergence
End-to-End Differentiable 6DoF Object Pose Estimation with Local and Global Constraints
Inferring the 6DoF pose of an object from a single RGB image is an important
but challenging task, especially under heavy occlusion. While recent approaches
improve upon the two stage approaches by training an end-to-end pipeline, they
do not leverage local and global constraints. In this paper, we propose
pairwise feature extraction to integrate local constraints, and triplet
regularization to integrate global constraints for improved 6DoF object pose
estimation. Coupled with better augmentation, our approach achieves state of
the art results on the challenging Occlusion Linemod dataset, with a 9%
improvement over the previous state of the art, and achieves competitive
results on the Linemod dataset.Comment: Accepted at the Workshop on Differentiable vision, graphics, and
physics applied to machine learning at Neurips 202
REDE: End-to-end Object 6D Pose Robust Estimation Using Differentiable Outliers Elimination
Object 6D pose estimation is a fundamental task in many applications.
Conventional methods solve the task by detecting and matching the keypoints,
then estimating the pose. Recent efforts bringing deep learning into the
problem mainly overcome the vulnerability of conventional methods to
environmental variation due to the hand-crafted feature design. However, these
methods cannot achieve end-to-end learning and good interpretability at the
same time. In this paper, we propose REDE, a novel end-to-end object pose
estimator using RGB-D data, which utilizes network for keypoint regression, and
a differentiable geometric pose estimator for pose error back-propagation.
Besides, to achieve better robustness when outlier keypoint prediction occurs,
we further propose a differentiable outliers elimination method that regresses
the candidate result and the confidence simultaneously. Via confidence weighted
aggregation of multiple candidates, we can reduce the effect from the outliers
in the final estimation. Finally, following the conventional method, we apply a
learnable refinement process to further improve the estimation. The
experimental results on three benchmark datasets show that REDE slightly
outperforms the state-of-the-art approaches and is more robust to object
occlusion