提出轻量级密集骨干网络，显著降低3D点云检测模型开销。
Rethinking Backbone Design for Lightweight 3D Object Detection in LiDAR
- 采用密集连接结构设计轻量骨干，兼顾速度与精度
- 在nuScenes上参数减少29%，延迟降28%，精度仅降2%
- 即插即用，适配现有3D检测模型无需修改
基于激光雷达的3D目标检测近年来推动了自动驾驶在真实环境中的发展。尽管性能优异，多数方法仍依赖VGG或ResNet类骨干网络，导致模型复杂度高。针对2D检测的轻量骨干研究成熟，但3D领域仍不足。本文提出Dense Backbone，一种专为点云数据设计的轻量级密集连接骨干，兼具高速、紧凑与强检测能力。将其适配至PillarNet等主流3D检测器，显著降低计算成本，同时保持主要检测性能。DensePillarNet在nuScenes测试集上实现29%参数减少和28%延迟下降，精度仅损失2%。该骨干可无缝集成至现有架构，无需改动其他模块。
原文摘要 · Abstract (English)
Recent advancements in LiDAR-based 3D object detection have significantly accelerated progress toward the realization of fully autonomous driving in real-world environments. Despite achieving high detection performance, most of the approaches still rely on a VGG-based or ResNet-based backbone for feature exploration, which increases the model complexity. Lightweight backbone design is well-explored for 2D object detection, but research on 3D object detection still remains limited. In this work, we introduce Dense Backbone, a lightweight backbone that combines the benefits of high processing speed, lightweight architecture, and robust detection accuracy. We adapt multiple SoTA 3d object detectors, such as PillarNet, with our backbone and show that with our backbone, these models retain most of their detection capability at a significantly reduced computational cost. To our knowledge, this is the first dense-layer-based backbone tailored specifically for 3D object detection from point cloud data. DensePillarNet, our adaptation of PillarNet, achieves a 29% reduction in model parameters and a 28% reduction in latency with just a 2% drop in detection accuracy on the nuScenes test set. Furthermore, Dense Backbone's plug-and-play design allows straightforward integration into existing architectures, requiring no modifications to other network components.
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