  Perceiving the physical world in 3D is fundamental for self-driving
applications. Although temporal motion is an invaluable resource to human
vision for detection, tracking, and depth perception, such features have not
been thoroughly utilized in modern 3D object detectors. In this work, we
propose a novel method for monocular video-based 3D object detection which
carefully leverages kinematic motion to improve precision of 3D localization.
Specifically, we first propose a novel decomposition of object orientation as
well as a self-balancing 3D confidence. We show that both components are
critical to enable our kinematic model to work effectively. Collectively, using
only a single model, we efficiently leverage 3D kinematics from monocular
videos to improve the overall localization precision in 3D object detection
while also producing useful by-products of scene dynamics (ego-motion and
per-object velocity). We achieve state-of-the-art performance on monocular 3D
object detection and the Bird's Eye View tasks within the KITTI self-driving
dataset.
