An Algorithm on Monocular 3D Object Detection Based on Depth Estimation

The traditional 2D convolution approaches are inappropriate for capturing information about local objects and their scale changes. To better depict 3D structures, the fundamental concept behind it is to mix depth information with RGB data to understand 3D space via depth estimations for creating depth maps of 2D images. However, it places a great deal of reliance on the precision of depth estimate, which results in subpar performance. In this paper, a monocular 3D object detection algorithm is proposed to resolve the issues mentioned earlier. The algorithm depends on the estimation of depth, which is essential for accurately detecting objects using a single camera. To begin with, a Normalization-based Attention Module has been deployed to a backbone network with two branches to extract RGB and depth maps separately. To merge the RGB and depth map features, a depth-guided dynamic local convolutional module is subsequently implemented. Depth map learns exemplar kernels from RGB images using a weight learning submodule and dilation convolution. Then it acts locally on the corresponding RGB images to solve the scale-sensitive problem of objects. By utilizing this approach, it becomes possible to extract feature representations from 2D images and map them onto 3D space, minimizing the disparity between the 2D and 3D representations. Experiments indicate that under the moderate setting of the class car - which is the most significant level of the KITTI dataset - our method outperforms other cutting-edge algorithms.