  Autonomous driving perception tasks rely heavily on cameras as the primary
sensor for Object Detection, Semantic Segmentation, Instance Segmentation, and
Object Tracking. However, RGB images captured by cameras lack depth
information, which poses a significant challenge in 3D detection tasks. To
supplement this missing data, mapping sensors such as LIDAR and RADAR are used
for accurate 3D Object Detection. Despite their significant accuracy, the
multi-sensor models are expensive and require a high computational demand. In
contrast, Monocular 3D Object Detection models are becoming increasingly
popular, offering a faster, cheaper, and easier-to-implement solution for 3D
detections. This paper introduces a different Multi-Tasking Learning approach
called MonoNext that utilizes a spatial grid to map objects in the scene.
MonoNext employs a straightforward approach based on the ConvNext network and
requires only 3D bounding box annotated data. In our experiments with the KITTI
dataset, MonoNext achieved high precision and competitive performance
comparable with state-of-the-art approaches. Furthermore, by adding more
training data, MonoNext surpassed itself and achieved higher accuracies.
