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Monocular 3D object detection is a crucial and challenging task forautonomous driving vehicle, while it uses only a single camera image to infer3D objects in the scene. To address the difficulty of predicting depth usingonly pictorial clue, we propose a novel perspective-aware convolutional layerthat captures long-range dependencies in images. By enforcing convolutionalkernels to extract features along the depth axis of every image pixel, weincorporates perspective information into network architecture. We integrateour perspective-aware convolutional layer into a 3D object detector anddemonstrate improved performance on the KITTI3D dataset, achieving a 23.9\%average precision in the easy benchmark. These results underscore theimportance of modeling scene clues for accurate depth inference and highlightthe benefits of incorporating scene structure in network design. Ourperspective-aware convolutional layer has the potential to enhance objectdetection accuracy by providing more precise and context-aware featureextraction.

Perspective-aware Convolution for Monocular 3D Object Detection