The field of computer vision is witnessing significant advancements in camera pose estimation and 3D scene generation. Researchers are exploring novel approaches to estimate the relative position of cameras and objects, enabling applications such as autonomous navigation, robotics, and augmented reality. One notable direction is the development of methods that can handle sparse-view scenarios, where only a limited number of images are available. Additionally, there is a growing interest in using deep learning techniques to generate high-quality 3D models from 2D sketches or images. Noteworthy papers in this area include NeuroLoc, which proposes a neurobiological camera location method, and One2Any, which estimates the relative 6-degrees of freedom object pose using only a single reference-single query RGB-D image. Another significant contribution is the development of DiffusionSfM, a data-driven multi-view reasoning approach that directly infers 3D scene geometry and camera poses from multi-view images. These innovative approaches are pushing the boundaries of what is possible in computer vision and have the potential to enable a wide range of applications. Notable papers include: NeuroLoc, which enhances robustness in complex environments and improves pose regression. One2Any, which achieves state-of-the-art accuracy and robustness in 6D object pose estimation.