Fade3D: Fast and Deployable 3D Object Detection for Autonomous Driving

Wei Ye,Qiming Xia,Hai Wu, Zhen Dong,Ruofei Zhong,Cheng Wang,Chenglu Wen

IEEE Transactions on Intelligent Transportation Systems（2025）CCF BSCI 1区SCI 2区

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Abstract

3D object detection is an essential scene perception capability for autonomous vehicles. In intelligent transportation systems, autonomous vehicles require minimal inference latency to sense their surroundings in real-time. However, advanced 3D detection methods often suffer from high inference latency. This limits the real-time deployment of 3D detection models in the real world. To address this problem, this paper proposes a fast and deployable 3D object detection method from the LiDAR point cloud for autonomous driving, named Fade3D. Firstly, we propose a Lightweight Input Encoder (LIE) to extract the most critical features from point clouds. Then, we develop a Spatial Feature Enhancement BEV backbone (SFENet) that efficiently encodes geometry features into compact representations. Additionally, we design an IoU-aware Loss Re-weighting (ILR) that enhances performance by shifting more attention to hard samples. Leveraging LIE and SFENet, our approach is independent of point cloud density and number, achieving significant speed advantages in processing large-scale point clouds and being deployment-friendly. Extensive experiments on KITTI and Waymo Open Dataset (WOD) datasets comparing various baseline detectors demonstrate its universality and superiority. Specifically, our method demonstrates impressive real-time inference capabilities, achieving 51.5 Hz on an RTX3090 GPU and 12.4 Hz on a Jetson Orin embedded development board. Code will be available at https://github.com/wayyeah/Fade3D

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Key words

3D object detection,point cloud,real-time inference,outdoor scene perception,autonomous driving

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【要点】：论文提出了一种名为Fade3D的快速且易于部署的3D物体检测方法，通过轻量级输入编码器、空间特征增强网络和IoU感知损失重加权策略，在自动驾驶领域实现了高效的LiDAR点云处理。

【方法】：作者使用轻量级输入编码器（LIE）提取点云的关键特征，并开发了一个空间特征增强BEV骨干网络（SFENet）以及一个IoU感知损失重加权（ILR）策略。

【实验】：在KITTI和Waymo Open Dataset（WOD）数据集上的广泛实验表明，该方法具有通用性和优越性，在RTX3090 GPU上实现了51.5 Hz的实时推理速度，在Jetson Orin嵌入式开发板上实现了12.4 Hz的实时推理速度。