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

Good 3D object detection performance from LiDAR-Camera sensors demandsseamless feature alignment and fusion strategies. We propose the 3DifFusionDetframework in this paper, which structures 3D object detection as a denoisingdiffusion process from noisy 3D boxes to target boxes. In this framework,ground truth boxes diffuse in a random distribution for training, and the modellearns to reverse the noising process. During inference, the model graduallyrefines a set of boxes that were generated at random to the outcomes. Under thefeature align strategy, the progressive refinement method could make asignificant contribution to robust LiDAR-Camera fusion. The iterativerefinement process could also demonstrate great adaptability by applying theframework to various detecting circumstances where varying levels of accuracyand speed are required. Extensive experiments on KITTI, a benchmark forreal-world traffic object identification, revealed that 3DifFusionDet is ableto perform favorably in comparison to earlier, well-respected detectors.

3DifFusionDet: Diffusion Model for 3D Object Detection with Robust LiDAR-Camera Fusion