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

In this work we propose 3D-FFS, a novel approach to make sensor fusion based3D object detection networks significantly faster using a class ofcomputationally inexpensive heuristics. Existing sensor fusion based networksgenerate 3D region proposals by leveraging inferences from 2D object detectors.However, as images have no depth information, these networks rely on extractingsemantic features of points from the entire scene to locate the object. Byleveraging aggregated intrinsic properties (e.g. point density) of point clouddata, 3D-FFS can substantially constrain the 3D search space and therebysignificantly reduce training time, inference time and memory consumptionwithout sacrificing accuracy. To demonstrate the efficacy of 3D-FFS, we haveintegrated it with Frustum ConvNet (F-ConvNet), a prominent sensor fusion based3D object detection model. We assess the performance of 3D-FFS on the KITTIdataset. Compared to F-ConvNet, we achieve improvements in training andinference times by up to 62.80% and 58.96%, respectively, while reducing thememory usage by up to 58.53%. Additionally, we achieve 0.36%, 0.59% and 2.19%improvements in accuracy for the Car, Pedestrian and Cyclist classes,respectively. 3D-FFS shows a lot of promise in domains with limited computingpower, such as autonomous vehicles, drones and robotics where LiDAR-Camerabased sensor fusion perception systems are widely used.

3D-FFS: Faster 3D object detection with Focused Frustum Search in sensor fusion based networks