FADet: 局所特徴注意に基づくマルチセンサー3D物体検出ネットワーク
FADet: A Multi-sensor 3D Object Detection Network based on Local Featured Attention
カメラ・LiDAR・レーダーそれぞれの特性に応じた局所特徴注意モジュールを設計し、これらを融合することで複雑な長尾シーンでも高精度な3D物体検出を実現した。
著者: Ziang Guo, Zakhar Yagudin, Selamawit Asfaw, Artem Lykov, Dzmitry Tsetserukou
分類: cs.CV, cs.RO
原文アブストラクト
Camera, LiDAR and radar are common perception sensors for autonomous driving tasks. Robust prediction of 3D object detection is optimally based on the fusion of these sensors. To exploit their abilities wisely remains a challenge because each of these sensors has its own characteristics. In this paper, we propose FADet, a multi-sensor 3D detection network, which specifically studies the characteristics of different sensors based on our local featured attention modules. For camera images, we propose dual-attention-based sub-module. For LiDAR point clouds, triple-attention-based sub-module is utilized while mixed-attention-based sub-module is applied for features of radar points. With local featured attention sub-modules, our FADet has effective detection results in long-tail and complex scenes from camera, LiDAR and radar input. On NuScenes validation dataset, FADet achieves state-of-the-art performance on LiDAR-camera object detection tasks with 71.8% NDS and 69.0% mAP, at the same time, on radar-camera object detection tasks with 51.7% NDS and 40.3% mAP. Code will be released at https://github.com/ZionGo6/FADet.
