ViT-BEVSeg: A Hierarchical Transformer Network for Monocular Birds-Eye-View Segmentation

• URL: http://arxiv.org/abs/2205.15667v1
• Date: Tue, 31 May 2022 10:18:36 GMT
• Title: ViT-BEVSeg: A Hierarchical Transformer Network for Monocular Birds-Eye-View Segmentation
• Authors: Pramit Dutta, Ganesh Sistu, Senthil Yogamani, Edgar Galv\'an and John McDonald
• Abstract summary: We evaluate the use of vision transformers (ViT) as a backbone architecture to generate Bird Eye View (BEV) maps. Our network architecture, ViT-BEVSeg, employs standard vision transformers to generate a multi-scale representation of the input image. We evaluate our approach on the nuScenes dataset demonstrating a considerable improvement relative to state-of-the-art approaches.
• Score: 2.70519393940262
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating a detailed near-field perceptual model of the environment is an important and challenging problem in both self-driving vehicles and autonomous mobile robotics. A Bird Eye View (BEV) map, providing a panoptic representation, is a commonly used approach that provides a simplified 2D representation of the vehicle surroundings with accurate semantic level segmentation for many downstream tasks. Current state-of-the art approaches to generate BEV-maps employ a Convolutional Neural Network (CNN) backbone to create feature-maps which are passed through a spatial transformer to project the derived features onto the BEV coordinate frame. In this paper, we evaluate the use of vision transformers (ViT) as a backbone architecture to generate BEV maps. Our network architecture, ViT-BEVSeg, employs standard vision transformers to generate a multi-scale representation of the input image. The resulting representation is then provided as an input to a spatial transformer decoder module which outputs segmentation maps in the BEV grid. We evaluate our approach on the nuScenes dataset demonstrating a considerable improvement in the performance relative to state-of-the-art approaches.

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