计算机科学
加权
激光雷达
特征(语言学)
人工智能
计算机视觉
对象(语法)
目标检测
模式识别(心理学)
遥感
医学
语言学
哲学
放射科
地质学
作者
Georgios Zamanakos,Lazaros Tsochatzidis,Angelos Amanatiadis,Ioannis Pratikakis
标识
DOI:10.1016/j.robot.2024.104664
摘要
3D object detection is a key element for the perception of autonomous vehicles. LiDAR sensors are commonly used to perceive the surrounding area, producing a sparse representation of the scene in the form of a point cloud. The current trend is to use deep learning neural network architectures that predict 3D bounding boxes. The vast majority of architectures process the LiDAR point cloud directly but, due to computation and memory constraints, at some point they compress the input to a 2D Bird’s Eye View (BEV) representation. In this work, we propose a novel 2D neural network architecture, namely the Feature Aware Re-weighting Network, for feature extraction in BEV using local context via an attention mechanism, to improve the 3D detection performance of LiDAR-based detectors. Extensive experiments on five state-of-the-art detectors and three benchmarking datasets, namely KITTI, Waymo and nuScenes, demonstrate the effectiveness of the proposed method in terms of both detection performance and minimal added computational burden. We release our code at https://github.com/grgzam/FAR.
科研通智能强力驱动
Strongly Powered by AbleSci AI