A Multi-Task Vision Transformer for Segmentation and Monocular Depth Estimation for Autonomous Vehicles

A Multi-Task Vision Transformer for Segmentation and Monocular Depth Estimation for Autonomous Vehicles

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In this paper, we investigate the use of Vision Transformers for processing and understanding visual data in an autonomous driving setting.Specifically, we explore the read more use of Vision Transformers for semantic segmentation and monocular depth estimation using only a single image as input.We present state-of-the-art Vision Transformers for these tasks and combine them into a multitask model.Through multiple experiments on four different street image datasets, we demonstrate that the multitask approach significantly reduces inference time while maintaining high accuracy for both tasks.

Additionally, we show that changing the size of the Transformer-based backbone can be used as a trade-off between inference speed and accuracy.Furthermore, we investigate the use of synthetic data for pre-training and show that it effectively increases the accuracy hindirochakkahaniya.com of the model when real-world data is limited.