  3D scene modeling techniques serve as the bedrocks in the geospatial
engineering and computer science, which drives many applications ranging from
automated driving, terrain mapping, navigation, virtual, augmented, mixed, and
extended reality (for gaming and movie industry etc.). This dissertation
presents a fraction of contributions that advances 3D scene modeling to its
state of the art, in the aspects of both appearance and geometry modeling. In
contrast to the prevailing deep learning methods, as a core contribution, this
thesis aims to develop algorithms that follow first principles, where
sophisticated physic-based models are introduced alongside with simpler
learning and inference tasks. The outcomes of these algorithms yield processes
that can consume much larger volume of data for highly accurate reconstructing
3D scenes at a scale without losing methodological generality, which are not
possible by contemporary complex-model based deep learning methods.
Specifically, the dissertation introduces three novel methodologies that
address the challenges of inferring appearance and geometry through
physics-based modeling.
  Overall, the research encapsulated in this dissertation marks a series of
methodological triumphs in the processing of complex datasets. By navigating
the confluence of deep learning, computational geometry, and photogrammetry,
this work lays down a robust framework for future exploration and practical
application in the rapidly evolving field of 3D scene reconstruction. The
outcomes of these studies are evidenced through rigorous experiments and
comparisons with existing state-of-the-art methods, demonstrating the efficacy
and scalability of the proposed approaches.
