Modern perception systems in the field of autonomous driving rely on 3D data analysis. LiDAR sensors are frequently used to acquire such data due to their increased resilience to different lighting conditions. Although rotating LiDAR scanners produce ring-shaped patterns in space, most networks analyze their data using an orthogonal voxel sampling strategy. This work presents a novel approach for analyzing 3D data produced by 360-degree depth scanners, utilizing a more suitable coordinate system, which is aligned with the scanning pattern. Furthermore, we intro-duce a novel notion of range-guided convolutions, adapting the receptive field by distance from the ego vehicle and the object's scale. Our network demonstrates powerful results on the competitive nuScenes 3D object detection challenge, comparable to current state-of-the-art architectures.