5,696 research outputs found
A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with Uncertainty
Accurate rotation estimation is at the heart of robot perception tasks such
as visual odometry and object pose estimation. Deep neural networks have
provided a new way to perform these tasks, and the choice of rotation
representation is an important part of network design. In this work, we present
a novel symmetric matrix representation of the 3D rotation group, SO(3), with
two important properties that make it particularly suitable for learned models:
(1) it satisfies a smoothness property that improves convergence and
generalization when regressing large rotation targets, and (2) it encodes a
symmetric Bingham belief over the space of unit quaternions, permitting the
training of uncertainty-aware models. We empirically validate the benefits of
our formulation by training deep neural rotation regressors on two data
modalities. First, we use synthetic point-cloud data to show that our
representation leads to superior predictive accuracy over existing
representations for arbitrary rotation targets. Second, we use image data
collected onboard ground and aerial vehicles to demonstrate that our
representation is amenable to an effective out-of-distribution (OOD) rejection
technique that significantly improves the robustness of rotation estimates to
unseen environmental effects and corrupted input images, without requiring the
use of an explicit likelihood loss, stochastic sampling, or an auxiliary
classifier. This capability is key for safety-critical applications where
detecting novel inputs can prevent catastrophic failure of learned models.Comment: In Proceedings of Robotics: Science and Systems (RSS'20), Corvallis ,
Oregon, USA, Jul. 12-16, 202
Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold
Single image pose estimation is a fundamental problem in many vision and
robotics tasks, and existing deep learning approaches suffer by not completely
modeling and handling: i) uncertainty about the predictions, and ii) symmetric
objects with multiple (sometimes infinite) correct poses. To this end, we
introduce a method to estimate arbitrary, non-parametric distributions on
SO(3). Our key idea is to represent the distributions implicitly, with a neural
network that estimates the probability given the input image and a candidate
pose. Grid sampling or gradient ascent can be used to find the most likely
pose, but it is also possible to evaluate the probability at any pose, enabling
reasoning about symmetries and uncertainty. This is the most general way of
representing distributions on manifolds, and to showcase the rich expressive
power, we introduce a dataset of challenging symmetric and nearly-symmetric
objects. We require no supervision on pose uncertainty -- the model trains only
with a single pose per example. Nonetheless, our implicit model is highly
expressive to handle complex distributions over 3D poses, while still obtaining
accurate pose estimation on standard non-ambiguous environments, achieving
state-of-the-art performance on Pascal3D+ and ModelNet10-SO(3) benchmarks
A Probabilistic Rotation Representation for Symmetric Shapes With an Efficiently Computable Bingham Loss Function
In recent years, a deep learning framework has been widely used for object
pose estimation. While quaternion is a common choice for rotation
representation, it cannot represent the ambiguity of the observation. In order
to handle the ambiguity, the Bingham distribution is one promising solution.
However, it requires complicated calculation when yielding the negative
log-likelihood (NLL) loss. An alternative easy-to-implement loss function has
been proposed to avoid complex computations but has difficulty expressing
symmetric distribution. In this paper, we introduce a fast-computable and
easy-to-implement NLL loss function for Bingham distribution. We also create
the inference network and show that our loss function can capture the symmetric
property of target objects from their point clouds.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessible. arXiv admin note: substantial text overlap with
arXiv:2203.0445
Ki-Pode: Keypoint-based Implicit Pose Distribution Estimation of Rigid Objects
The estimation of 6D poses of rigid objects is a fundamental problem in
computer vision. Traditionally pose estimation is concerned with the
determination of a single best estimate. However, a single estimate is unable
to express visual ambiguity, which in many cases is unavoidable due to object
symmetries or occlusion of identifying features. Inability to account for
ambiguities in pose can lead to failure in subsequent methods, which is
unacceptable when the cost of failure is high. Estimates of full pose
distributions are, contrary to single estimates, well suited for expressing
uncertainty on pose. Motivated by this, we propose a novel pose distribution
estimation method. An implicit formulation of the probability distribution over
object pose is derived from an intermediary representation of an object as a
set of keypoints. This ensures that the pose distribution estimates have a high
level of interpretability. Furthermore, our method is based on conservative
approximations, which leads to reliable estimates. The method has been
evaluated on the task of rotation distribution estimation on the YCB-V and
T-LESS datasets and performs reliably on all objects.Comment: 11 pages, 2 figure
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