Best Paper Award at Embedded Vision Workshop
We are proud to share that our paper, “Dense Backbone: A Lightweight Dense Layer-Based Architecture for LiDAR 3D Object Detection,” authored by Adwait Chandorkar, Hasan Tercan, and Prof. Tobias Meisen, received the Best Paper Award at the Embedded Vision Workshop (EVW) held in October.
The paper introduces Dense Backbone, a novel lightweight architecture that leverages dense connections to enhance feature reuse and reduce computational load in LiDAR-based 3D object detection — a critical step toward efficient perception in autonomous systems. The work was presented by Adwait Chandorkar, who also showcased the accompanying poster at the workshop. We at TMDT are grateful for the recognition and encouraged to continue advancing efficient AI for embedded and real-world applications.
