  This dissertation is a multifaceted contribution to the advancement of
vision-based 3D perception technologies. In the first segment, the thesis
introduces structural enhancements to both monocular and stereo 3D object
detection algorithms. By integrating ground-referenced geometric priors into
monocular detection models, this research achieves unparalleled accuracy in
benchmark evaluations for monocular 3D detection. Concurrently, the work
refines stereo 3D detection paradigms by incorporating insights and inferential
structures gleaned from monocular networks, thereby augmenting the operational
efficiency of stereo detection systems. The second segment is devoted to
data-driven strategies and their real-world applications in 3D vision
detection. A novel training regimen is introduced that amalgamates datasets
annotated with either 2D or 3D labels. This approach not only augments the
detection models through the utilization of a substantially expanded dataset
but also facilitates economical model deployment in real-world scenarios where
only 2D annotations are readily available. Lastly, the dissertation presents an
innovative pipeline tailored for unsupervised depth estimation in autonomous
driving contexts. Extensive empirical analyses affirm the robustness and
efficacy of this newly proposed pipeline. Collectively, these contributions lay
a robust foundation for the widespread adoption of vision-based 3D perception
technologies in autonomous driving applications.
