# StripDet: Strip Attention-Based Lightweight 3D Object Detection from Point Cloud Source: [StripDet: Strip Attention-Based Lightweight 3D Object Detection from Point Cloud]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:34:43.561Z Authors: Weichao Wang, Wendong Mao, Zhongfeng Wang Published: 2025-09-07T07:32:31.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()