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

The use of knowledge distillation in monocular 3D object detection has been explored by incorporating a LiDAR model as the teacher network to transfer knowledge to a monocular network. However, LiDAR data and images belong to distinct data types, and their respective models exhibit significant structural disparities. These differences serve as constraints to the complete and comprehensive transmission of depth information from the teacher network to the student network. To overcome these limitations, we propose an end-to-end network with Comprehensive Feature Knowledge Distillation (CFKD) monocular pseudo-LiDAR. This method transforms monocular images into pseudo-LiDAR and feeds them into a student LiDAR network which receives distilled knowledge from a teacher LiDAR network. By leveraging the similarity in the network structures of the teacher and student LiDAR networks, our approach efficiently utilizes the LiDAR information via comprehensive distillation of features. We assessed our method&#x2019;s efficient implementation on the kitti3D dataset. Our methods achieved an improvement of 4.67 for AP BEV  in the moderate category and 2.65 for AP BEV  in the hard category on the test set.

Monocular 3D Object Detection from Comprehensive Feature Distillation Pseudo-LiDAR