Summary of Addressing Diverging Training Costs Using Bevrestore For High-resolution Bird’s Eye View Map Construction, by Minsu Kim et al.
Addressing Diverging Training Costs using BEVRestore for High-resolution Bird’s Eye View Map Construction
by Minsu Kim, Giseop Kim, Sunwook Choi
First submitted to arxiv on: 2 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper tackles the challenge of constructing high-resolution Bird’s Eye View (BEV) maps for urban environments. Recent advancements in BEV fusion have achieved remarkable mapping results, but their deep and bulky architecture incurs substantial backpropagation memory and computing latency costs. This issue, known as the diverging training costs problem, has led most existing methods to adopt low-resolution BEV, resulting in imprecise location estimation for urban scene components like road lanes and sidewalks. The authors propose a novel BEVRestore mechanism to address this issue, which encodes features to LR BEV space and restores them to HR space for memory-efficient map construction. They introduce the BEV restoration strategy, which restores aliasing and blocky artifacts of up-scaled BEV features and narrows down label width. Extensive experiments demonstrate that the proposed mechanism provides a plug-and-play, memory-efficient pipeline for high-resolution map construction with broad BEV scope. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to solve a big problem in making detailed maps of cities. Right now, computers need too much memory and processing time to create these maps accurately. This makes it hard for robots or self-driving cars to plan safe routes. The authors developed a new way to make map construction faster and more efficient using something called BEVRestore. They tested this approach on city maps and showed that it works well. |
Keywords
» Artificial intelligence » Backpropagation
