  Recent advancements in Bird's Eye View (BEV) fusion for map construction have
demonstrated remarkable mapping of urban environments. However, their deep and
bulky architecture incurs substantial amounts of backpropagation memory and
computing latency. Consequently, the problem poses an unavoidable bottleneck in
constructing high-resolution (HR) BEV maps, as their large-sized features cause
significant increases in costs including GPU memory consumption and computing
latency, named diverging training costs issue. Affected by the problem, most
existing methods adopt low-resolution (LR) BEV and struggle to estimate the
precise locations of urban scene components like road lanes, and sidewalks. As
the imprecision leads to risky self-driving, the diverging training costs issue
has to be resolved. In this paper, we address the issue with our novel Trumpet
Neural Network (TNN) mechanism. The framework utilizes LR BEV space and outputs
an up-sampled semantic BEV map to create a memory-efficient pipeline. To this
end, we introduce Local Restoration of BEV representation. Specifically, the
up-sampled BEV representation has severely aliased, blocky signals, and thick
semantic labels. Our proposed Local Restoration restores the signals and thins
(or narrows down) the width of the labels. Our extensive experiments show that
the TNN mechanism provides a plug-and-play memory-efficient pipeline, thereby
enabling the effective estimation of real-sized (or precise) semantic labels
for BEV map construction.
