StripDet: Strip Attention-Based Lightweight 3D Object Detection from Point Cloud

• URL: http://arxiv.org/abs/2509.05954v1
• Date: Sun, 07 Sep 2025 07:32:31 GMT
• Title: StripDet: Strip Attention-Based Lightweight 3D Object Detection from Point Cloud
• Authors: Weichao Wang, Wendong Mao, Zhongfeng Wang,
• Abstract summary: StripDet is a novel lightweight framework designed for on-device efficiency.<n>By decomposing standard 2D convolutions into asymmetric strip convolutions, SAB efficiently extracts directional features.<n>Our model achieves a 79.97% mAP for car detection, surpassing the baseline PointPillars with a 7x parameter reduction.
• Score: 6.513870680888872
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The deployment of high-accuracy 3D object detection models from point cloud remains a significant challenge due to their substantial computational and memory requirements. To address this, we introduce StripDet, a novel lightweight framework designed for on-device efficiency. First, we propose the novel Strip Attention Block (SAB), a highly efficient module designed to capture long-range spatial dependencies. By decomposing standard 2D convolutions into asymmetric strip convolutions, SAB efficiently extracts directional features while reducing computational complexity from quadratic to linear. Second, we design a hardware-friendly hierarchical backbone that integrates SAB with depthwise separable convolutions and a simple multiscale fusion strategy, achieving end-to-end efficiency. Extensive experiments on the KITTI dataset validate StripDet's superiority. With only 0.65M parameters, our model achieves a 79.97% mAP for car detection, surpassing the baseline PointPillars with a 7x parameter reduction. Furthermore, StripDet outperforms recent lightweight and knowledge distillation-based methods, achieving a superior accuracy-efficiency trade-off while establishing itself as a practical solution for real-world 3D detection on edge devices.

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