SCN-Pillar: Construct a Pillar-based Fully Sparse Lightweight 3D Detector via Sparse ConvNeXt
Xusheng Li, Chengliang Wang, Tian Jiang, Yonggang Luo, Bo Zheng · 2025
Since autonomous driving requires high-precision object detection in real-time, and multi-line LiDAR generates huge point clouds, developing a lightweight 3D detector is crucial. The high sparsity and unstructured nature of point clouds require transforming raw data into a structured format for effective feature extraction. Nevertheless, despite the decrease in computational complexity achieved through the transformation, the resulting structure exhibits high sparsity. Consequently, using conventional neural networks for detectors necessitates substantial additional computational resources. Voxel-based detectors densely partition the point cloud in the height space and must use 3D convolutions. Therefore, compared with pillar-based 3D detectors, voxel-based 3D detectors generally achieve higher object detection accuracy, but their detection speed is much slower. Given these challenges, we propose a fully sparse ConvNeXt block for more efficient pillar feature extraction that selectively extracts features from effective data positions. We have developed SCN-Pillar, a pillar-based, fully sparse, lightweight 3D detector that adopts the sparse ConvNeXt. The SCN-Pillar has been validated on the Waymo open dataset, showcasing enhancements in accuracy across a range of object detection tasks. The APH improvement in pedestrian detection has reached more than 1.2. It only requires the computational cost of the pillar-based solution, yet its object detection accuracy exceeds that of the voxel-based solution. The object detection speed reaches 18.28 FPS. The code is available at https://github.com/kaikailab/SCN-Pillar.