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

Widely-used LiDAR-based 3D object detectors often neglect fundamentalgeometric information readily available from the object proposals in theirconfidence estimation. This is mostly due to architectural design choices,which were often adopted from the 2D image domain, where geometric context israrely available. In 3D, however, considering the object properties and itssurroundings in a holistic way is important to distinguish between true andfalse positive detections, e.g. occluded pedestrians in a group. To addressthis, we present GACE, an intuitive and highly efficient method to improve theconfidence estimation of a given black-box 3D object detector. We aggregategeometric cues of detections and their spatial relationships, which enables usto properly assess their plausibility and consequently, improve the confidenceestimation. This leads to consistent performance gains over a variety ofstate-of-the-art detectors. Across all evaluated detectors, GACE proves to beespecially beneficial for the vulnerable road user classes, i.e. pedestriansand cyclists.

GACE: Geometry Aware Confidence Enhancement for Black-Box 3D Object Detectors on LiDAR-Data