Intrinsic-feature-guided 3D Object Detection

• URL: http://arxiv.org/abs/2504.00382v1
• Date: Tue, 01 Apr 2025 02:54:06 GMT
• Title: Intrinsic-feature-guided 3D Object Detection
• Authors: Wanjing Zhang, Chenxing Wang,
• Abstract summary: This paper presents an intrinsic-feature-guided 3D object detection method based on a template-assisted feature enhancement module.<n>Proposal-level contrastive learning mechanism is designed to enhance the feature differences between foreground and background objects.
• Score: 3.255688303169846
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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