Bird’s Eye View Segmentation Using Lifted 2D Semantic Features

Isht Dwived, Srikanth Malla, Yi-Ting Chen, Behzad Behzad Dariush

研究成果: Paper同行評審

摘要

We consider the problem of Bird’s Eye View (BEV) segmentation with perspective
monocular camera view as input. An effective solution to this problem is important
in many autonomous navigation tasks such as behavior prediction and planning, being
that the BEV segmented image provides an explainable intermediate representation that
captures both the geometry and layout of the surrounding scene. Our approach to this
problem involves a novel BEV feature transformation layer that effectively exploits depth maps to transform 2D image features to the BEV space. The framework includes the design of a neural network architecture to produce BEV segmentation maps using the proposed transformation layer. Of particular interest is evaluation of the proposed method in
complex scenarios involving highly unstructured scenes that are not represented in static
maps. In the absence of an appropriate dataset for this task, we introduce the EPOSH
road-scene dataset that consists of 560 video-clips of highly unstructured construction
scenes, annotated with unique labels in both perspective and BEV. For evaluation, we
compare our approach with several competitive baselines and recently published works
and show improvement over state of the art on the Nuscenes and on our EPOSH dataset.
We plan to release the dataset, code and trained models at https://usa.honda-ri.com/eposh.
原文English
頁面1-13
頁數13
出版狀態Published - 11月 2021

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