Depth Estimation using DNN Architecture and Vision-Based Transformers

Abstract

Depth Estimation is the process of estimating the depth of objects with a 2D image as an input. The importance of Depth Estimation is that it provides some critical information about the 3D structure of a scene given a sequence of 2D images. This is helpful in various applications like robotics, virtual reality, autonomous driving, medical imaging, and so on and so forth. Our main objective here is to make the environment more perceivable by autonomous vehicles. Unlike the traditional approaches which try to extract depth from the input image, we apply instance segmentation to the image and then use the segmented image as an input to the depth map generator. In this paper, we use a fully connected neural network with dense prediction transformers. As mentioned above, the image after instance segmentation is given as an input for the transformers. The Transformer is the backbone for producing high-resolution images. Our experimentation results have shown many improvements related to the details and sharpness of the image as compared to the traditional depth estimation techniques. The architecture is applied and tested on the KITTI data set.