Rethinking Backbone Design for Lightweight 3D Object Detection in LiDAR

2025-08-01

AI Summary

To address excessive computational overhead and parameter redundancy in LiDAR point cloud 3D detectors’ backbone networks, this paper proposes Dense Backbone—a lightweight, densely connected backbone specifically designed for 3D detection. It is the first to introduce dense connectivity into 3D detection backbone architecture while preserving plug-and-play compatibility—requiring no modifications to the detection head or other components. Built upon the pillar-based representation, Dense Backbone integrates lightweight convolutions with cross-layer feature reuse mechanisms to enhance representational efficiency. Evaluated on the nuScenes benchmark, DensePillarNet—a detector incorporating Dense Backbone—achieves a 29% reduction in model parameters and a 28% decrease in inference latency compared to the baseline, with only a marginal 2% drop in mAP. This yields a significantly improved accuracy–efficiency trade-off, making it particularly suitable for resource-constrained autonomous driving deployment scenarios.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Hardware-aware ML
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Design lightweight backbone for 3D LiDAR object detection
• Reduce model complexity while maintaining detection accuracy
• Enable plug-and-play integration into existing 3D detectors

Innovation

• Lightweight dense-layer backbone for 3D detection
• Plug-and-play design reduces computational cost
• Maintains accuracy with fewer parameters and latency