Locating vehicles, targets and objects in three-dimensional space is key to many fields of science and engineering such as robotics, aerospace, computer vision and graphics. Rotations and poses (position plus orientation) of bodies can be expressed in a variety of ways. Rotation matrices constitute one of the classic matrix Lie groups, the special orthogonal group—
SO
(
3
)
. Poses can likewise be represented by matrices. One such representation is embodied in a 4
×
4 matrix establishing another famous matrix Lie group, the special Euclidean group—
SE
(
3
)
. An alternative representation of pose uses 6
×
6 matrices and is referred to as the group of pose adjoints—
Ad
(
SE
(
3
)
)
. The eigenstructures of these representations reveal much about them from Euler’s theorem for rotations to the Mozzi–Chasles theorem for the general displacement of a rigid body. While the eigenstructure of
SO
(
3
)
has been extensively studied, those of
SE
(
3
)
and
Ad
(
SE
(
3
)
)
have hardly received the same scrutiny yet their structure is much richer. Motivated by their importance in kinematics and dynamics, we provide here a complete characterization of rotations and poses in terms of the eigenstructure of their matrix Lie group representations. An eigendecomposition of pose matrices reveals that they can be cast into a form similar to that of rotations although the structure of the former can vary depending on the nature of the pose involved. In particular, the pose matrices of
SE
(
3
)
and
Ad
(
SE
(
3
)
)
cannot generally be diagonalized as can rotation matrices but they of course do yield to a Jordan normal form, from which we can identify a principal-axis pose in much the same manner that we can a principal-axis rotation. We also address the minimal polynomials for poses and derive a novel expression for the Jacobian in
Ad
(
SE
(
3
)
)
. Finally, we argue that the true counterpart to
SO
(
3
)
for poses is not
SE
(
3
)
but
Ad
(
SE
(
3
)
)
.