Problem
Research questions and friction points this paper is trying to address.
3D object detection
LiDAR point clouds
voxel-based methods
point-based methods
computational bottlenecks
PointLAM: Local Attentive Mamba for Efficient Point-based 3D Object Detection
🤖 AI Summary
为解决点云3D目标检测中的效率与精度问题，提出PointLAM架构，通过Laplacian点采样和局部Hadamard聚合器提高计算效率和检测精度。
📝 Abstract
3D object detection from LiDAR point clouds faces a fundamental dilemma: voxel-based methods achieve efficiency at the cost of geometric quantization, while point-based methods preserve fidelity but suffer from prohibitive computational bottlenecks. Specifically, point-based architectures are crippled by slow downsampling strategies (e.g., FPS) and expensive dynamic neighbor queries (e.g., k-NN) coupled with costly continuous interactions. To tackle these systemic inefficiencies, we propose PointLAM, a highly efficient and powerful point-based architecture driven by two synergistic innovations. First, to resolve the downsampling bottleneck, we develop the Laplacian Point Sampler (LPS). LPS employs an implicit discrete Laplacian high-pass filter and Doubly Sorted Sampling to achieve fast, structure-aware foreground preservation. Second, to overcome local modeling latency, we design the Local Hadamard Aggregator (LHA). LHA decouples spatial indexing from feature representation using transient grids, and replaces complex continuous interactions with a Hadamard Gating mechanism for topology-aware, attentive modulation. By coupling this local gating with Bi-Directional Mamba (BDM) layers for global sequence modeling, we formulate the Local Attentive Mamba (LAM) block. Powered by this architecture, PointLAM achieves competitive performance on nuScenes and Waymo for point-based detectors. It rivals highly optimized voxel competitors while requiring a fraction of the computational footprint, demonstrating marked superiority in detecting small instances and handling extreme sparsity. Project page: https://pointlam.github.io/.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Laplacian Point Sampler
Local Hadamard Aggregator
Hadamard Gating
Bi-Directional Mamba
🔎 Similar Papers
No similar papers found.
