Label-free optical imaging is a noninvasive way to study the natural structure of cells, tissues, and materials. One optical phenomenon that enables label-free imaging is birefringence, which arises from anisotropic variations in refractive index due to ordered molecular or fibrous architectures, such as collagen or muscle fibers in biological tissues. Despite its significance, most existing polarization-based birefringence imaging systems capture only two-dimensional (2D) projections, thus missing the complete three-dimensional (3D) information needed to fully characterize anisotropic structures. Since optical birefringence is inherently direction-dependent, complete angular data are essential for reconstructing the underlying 3D organization. This dissertation addresses these limitations by introducing volumetric birefringence tomography through polarized light field microscopy (PLFM). By collecting both spatial and angular information in a single snapshot using a microlens array, PLFM provides a rich dataset that reveals how anisotropic materials modify the polarization state along each ray's path. Three complementary computational strategies—a geometrical optics model, a wave optics model, and a deep learning approach—have been developed to translate these angle-resolved signals into label-free 3D birefringence maps at high resolution. This includes both the magnitude of birefringence and the complete 3D orientation of anisotropic structures within thick samples. Comprehensive validations on both synthetic phantoms and biological specimens confirm that PLFM-based volumetric imaging accurately captures the relevant anisotropy parameters in three dimensions. By reconstructing 3D birefringence maps, this technique provides novel, label-free insights into microstructural organization. These results highlight the strong potential of PLFM for advancing investigations in fundamental research, tissue engineering, and clinical diagnostics, where high-fidelity, quantitative analyses of anisotropic materials are paramount.