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.

本论文通过引入基于物理的模型，介绍了三种新方法来解决通过物理建模推断外观和几何的挑战，从而在处理复杂数据集方面取得了一系列方法上的胜利，为未来在3D场景重建的快速发展领域中的探索和实际应用奠定了一个强大的框架。

基于透视成像的可伸缩场景建模：基于物理的外观和几何推理