  Monocular 3D object detection is a crucial and challenging task for
autonomous driving vehicle, while it uses only a single camera image to infer
3D objects in the scene. To address the difficulty of predicting depth using
only pictorial clue, we propose a novel perspective-aware convolutional layer
that captures long-range dependencies in images. By enforcing convolutional
kernels to extract features along the depth axis of every image pixel, we
incorporates perspective information into network architecture. We integrate
our perspective-aware convolutional layer into a 3D object detector and
demonstrate improved performance on the KITTI3D dataset, achieving a 23.9\%
average precision in the easy benchmark. These results underscore the
importance of modeling scene clues for accurate depth inference and highlight
the benefits of incorporating scene structure in network design. Our
perspective-aware convolutional layer has the potential to enhance object
detection accuracy by providing more precise and context-aware feature
extraction.

Computer Vision and Pattern Recognition