The field of perception systems is rapidly advancing, with a focus on developing more accurate and robust methods for 3D reconstruction, object detection, and tracking. Recent research has explored the use of Neural Radiance Fields (NeRF) for 3D reconstruction, including applications in agriculture and industrial automation. Additionally, there has been significant progress in radar-based perception systems, including the development of new methods for volumetric reconstruction and object detection. These advancements have the potential to improve the accuracy and reliability of perception systems in a variety of applications, including autonomous driving and industrial automation. Notable papers include: NeRF-based Point Cloud Reconstruction using a Stationary Camera for Agricultural Applications, which presents a novel method for 3D reconstruction using a stationary camera. SpINR: Neural Volumetric Reconstruction for FMCW Radars, which introduces a new framework for volumetric reconstruction using FMCW radar data. AttentiveGRU: Recurrent Spatio-Temporal Modeling for Advanced Radar-Based BEV Object Detection, which proposes a novel attention-based recurrent approach for object detection in radar data.