A Convolution-Involution Hybrid Framework for Monocular 3D Object Detection

Monocular 3D object detection is a prevalent research topic with several issues yet to be explored. In this paper, we present a novel framework for monocular 3D object detection with autonomous driving as the target application. As the first contribution, wed esign a novel Convolution-Involution Hybrid module for the 3D regression of geometric properties. The peculiar characteristics of involutions are delicately combined with the mature design of convolutions to yield enhanced network performance. The second contribution of us is the Dynamic Error- Tolerant loss. A tolerant threshold is integrated into the disentangled L1 loss to allow for minor errors from the network output. The threshold gradually changes in accordance with both the training iterations and the depth of objects to diminish the impact of outlying objects. To corroborate the effectiveness of the proposed method, extensive experiments and ablation study have been conducted on the prestigious KITTI dataset, and the experimental results demonstrate that our network outperforms the baseline by a large margin while retaining a real-time inference speed.