用物体结构模板增强点云特征，提升自动驾驶3D检测精度
Intrinsic-feature-guided 3D Object Detection
- 基于通用模板提取物体内在特征，补全点云不完整结构
- 通过对比学习强化前景与背景特征差异，提升区分能力
- 可插拔模块，适配多种检测模型，显著提升性能
基于激光雷达的3D目标检测对自动驾驶系统至关重要。然而，激光雷达点云常呈现稀疏、分布不均和结构不完整等问题，严重制约检测性能。在道路场景中，车辆、行人和非机动车具有规则的网格与拓扑结构，适合通过完整模板引导来增强表征。为此，本文提出一种基于模板辅助特征增强模块的内在特征引导3D检测方法，从通用模板中提取内在特征，为前景物体提供丰富的结构信息。此外，设计了提案级对比学习机制，增强前景与背景物体间的特征差异。所提模块可作为即插即用组件，提升多种现有方法的性能。大量实验表明，该方法达到极具竞争力的检测效果。代码将发布于 https://github.com/zhangwanjingjj/IfgNet.git。
原文摘要 · Abstract (English)
LiDAR-based 3D object detection is essential for autonomous driving systems. However, LiDAR point clouds may appear to have sparsity, uneven distribution, and incomplete structures, significantly limiting the detection performance. In road driving environments, target objects referring to vehicles, pedestrians and cyclists are well-suited for enhancing representation through the complete template guidance, considering their grid and topological structures. Therefore, this paper presents an intrinsic-feature-guided 3D object detection method based on a template-assisted feature enhancement module, which extracts intrinsic features from relatively generalized templates and provides rich structural information for foreground objects. Furthermore, a proposal-level contrastive learning mechanism is designed to enhance the feature differences between foreground and background objects. The proposed modules can act as plug-and-play components and improve the performance of multiple existing methods. Extensive experiments illustrate that the proposed method achieves the highly competitive detection results. Code will be available at https://github.com/zhangwanjingjj/IfgNet.git.
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