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3D-LaneNet- End-to-End 3D Multiple Lane Detection.pdf
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上传时间: 2020-04-20
详细说明：We introduce a network that directly predicts the 3D layout of lanes in a road scene from a single image. This
work marks a first attempt to address this task with onboard sensing without assuming a known constant lane
width or relying on pre-mapped environments. Our network
architecture, 3D-LaneNet, applies two new concepts: intranetwork inverse-perspective mapping (IPM) and anchorbased lane representation. The intra-network IPM projection facilitates a dual-representation information flow
in both regular image-view and top-view. An anchor-percolumn output representation enables our end-to-end approach which replaces common heuristics such as clustering and outlier rejection, casting lane estimation as an object detection problem. In addition, our approach explicitly
handles complex situations such as lane merges and splits.
Results are shown on two new 3D lane datasets, a synthetic
and a real one. For comparison with existing methods, we
test our approach on the image-only tuSimple lane detection
benchmark, achieving performance competitive with stateof-the-art
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