  Monocular vision-based 3D object detection is crucial in various sectors, yet
existing methods face significant challenges in terms of accuracy and
computational efficiency. Building on the successful strategies in 2D detection
and depth estimation, we propose MonoDETRNext, which seeks to optimally balance
precision and processing speed. Our methodology includes the development of an
efficient hybrid visual encoder, enhancement of depth prediction mechanisms,
and introduction of an innovative query generation strategy, augmented by an
advanced depth predictor. Building on MonoDETR, MonoDETRNext introduces two
variants: MonoDETRNext-F, which emphasizes speed, and MonoDETRNext-A, which
focuses on precision. We posit that MonoDETRNext establishes a new benchmark in
monocular 3D object detection and opens avenues for future research. We
conducted an exhaustive evaluation demonstrating the model's superior
performance against existing solutions. Notably, MonoDETRNext-A demonstrated a
4.60% improvement in the AP3D metric on the KITTI test benchmark over MonoDETR,
while MonoDETRNext-F showed a 2.21% increase. Additionally, the computational
efficiency of MonoDETRNext-F slightly exceeds that of its predecessor.
