English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Building 3D perception systems for autonomous vehicles that do not rely onhigh-density LiDAR is a critical research problem because of the expense ofLiDAR systems compared to cameras and other sensors. Recent research hasdeveloped a variety of camera-only methods, where features are differentiably"lifted" from the multi-camera images onto the 2D ground plane, yielding a"bird's eye view" (BEV) feature representation of the 3D space around thevehicle. This line of work has produced a variety of novel "lifting" methods,but we observe that other details in the training setups have shifted at thesame time, making it unclear what really matters in top-performing methods. Wealso observe that using cameras alone is not a real-world constraint,considering that additional sensors like radar have been integrated into realvehicles for years already. In this paper, we first of all attempt to elucidatethe high-impact factors in the design and training protocol of BEV perceptionmodels. We find that batch size and input resolution greatly affectperformance, while lifting strategies have a more modest effect -- even asimple parameter-free lifter works well. Second, we demonstrate that radar datacan provide a substantial boost to performance, helping to close the gapbetween camera-only and LiDAR-enabled systems. We analyze the radar usagedetails that lead to good performance, and invite the community to re-considerthis commonly-neglected part of the sensor platform.

Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?