State-of-the-art evaluation of 'Shape from Polarisation' methods

Advancements in the fields of Computer Vision and Artificial Intelligence have resulted in the emergence of innovative techniques for the extraction of three-dimensional shape information. One such technique is Shape from Polarization (SfP), which tries to exploit polarization patterns of light to achieve its goal. This thesis provides a comprehensive analysis of the most recent state-of-the-art SfP techniques, with a particular focus on comparing three physics-based methodologies and a hybrid approach that fuses Deep Learning with physics-based modelling. The study aims to assess the strengths and limitations of each technique through a systematic assessment evaluation using a dataset of rendered images. To ensure fair comparison and evaluation, a tailored dataset of synthetically created polarization images was created using the Mitsuba 3 rendering engine. The comparative analysis utilizing the tailor-made dataset provides valuable insights into the performance of each technique. This serves as a useful guide for researchers and practitioners in selecting appropriate methods based on specific application requirements, while also providing insight into the usability or otherwise of these methods in real-world contexts.