The field of computer vision and geometric computing is witnessing a significant shift towards more efficient and robust methods for 3D visual computing and visual localization. Researchers are exploring new paradigms that decouple 3D coordinates from camera poses, allowing for more accurate and reliable structure from motion and absolute pose estimation. Notably, the use of geometric representations and algebraic methods is becoming increasingly popular, enabling the development of more generalizable and computationally efficient algorithms. One of the key trends in this area is the use of rotation-only optimization frameworks and geometric representation regression, which have been shown to improve the accuracy and robustness of visual localization and 3D reconstruction. Additionally, the application of geometric algebra methods is being explored for its potential to enhance the fidelity and efficiency of character animation, rendering, and neural rendering in extended reality environments. Some noteworthy papers in this area include: GRLoc: Geometric Representation Regression for Visual Localization, which proposes a geometrically-grounded approach to absolute pose regression. One algebra for all: Geometric Algebra methods for neurosymbolic XR scene authoring, animation and neural rendering, which explores the transformative role of geometric algebra in advancing computer graphics and extended reality.