Addressing Diverging Training Costs using Local Restoration for Precise Bird's Eye View Map Construction

arXiv:2405.01016v1 Announce Type: new
Abstract: 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 …

abstract architecture arxiv backpropagation bird computing construction costs cs.ai cs.cv environments fusion however latency map mapping memory restoration training training costs type urban view