  Since the introduction of the self-attention mechanism and the adoption of
the Transformer architecture for Computer Vision tasks, the Vision
Transformer-based architectures gained a lot of popularity in the field, being
used for tasks such as image classification, object detection and image
segmentation. However, efficiently leveraging the attention mechanism in vision
transformers for the Monocular 3D Object Detection task remains an open
question. In this paper, we present LAM3D, a framework that Leverages
self-Attention mechanism for Monocular 3D object Detection. To do so, the
proposed method is built upon a Pyramid Vision Transformer v2 (PVTv2) as
feature extraction backbone and 2D/3D detection machinery. We evaluate the
proposed method on the KITTI 3D Object Detection Benchmark, proving the
applicability of the proposed solution in the autonomous driving domain and
outperforming reference methods. Moreover, due to the usage of self-attention,
LAM3D is able to systematically outperform the equivalent architecture that
does not employ self-attention.
