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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Object detection and motion parameters estimation are crucial tasks forself-driving vehicle safe navigation in a complex urban environment. In thiswork we propose a novel real-time approach of temporal context aggregation formotion detection and motion parameters estimation based on 3D point cloudsequence. We introduce an ego-motion compensation layer to achieve real-timeinference with performance comparable to a naive odometric transform of theoriginal point cloud sequence. Not only is the proposed architecture capable ofestimating the motion of common road participants like vehicles or pedestriansbut also generalizes to other object categories which are not present intraining data. We also conduct an in-deep analysis of different temporalcontext aggregation strategies such as recurrent cells and 3D convolutions.Finally, we provide comparison results of our state-of-the-art model withexisting solutions on KITTI Scene Flow dataset.

Any Motion Detector: Learning Class-agnostic Scene Dynamics from a Sequence of LiDAR Point Clouds