PointLAM: Local Attentive Mamba for Efficient Point-based 3D Object Detection

3D object detection from LiDAR point clouds faces a fun-damental dilemma: voxel-based methods achieve efficiency at the costof geometric quantization, while point-based methods preserve fidelitybut 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 withcostly continuous interactions. To tackle these systemic inefficiencies, wepropose PointLAM, a highly efficient and powerful point-based architec-ture driven by two synergistic innovations. First, to resolve the downsam-pling bottleneck, we develop the Laplacian Point Sampler (LPS). LPSemploys an implicit discrete Laplacian high-pass filter and Doubly SortedSampling to achieve fast, structure-aware foreground preservation. Sec-ond, to overcome local modeling latency, we design the Local HadamardAggregator (LHA). LHA decouples spatial indexing from feature repre-sentation using transient grids, and replaces complex continuous interac-tions with a Hadamard Gating mechanism for topology-aware, attentivemodulation. By coupling this local gating with Bi-Directional Mamba(BDM) layers for global sequence modeling, we formulate the Local At-tentive Mamba (LAM) block. Powered by this architecture, PointLAMachieves competitive performance on nuScenes and Waymo for point-based detectors. It rivals highly optimized voxel competitors while re-quiring a fraction of the computational footprint, demonstrating markedsuperiority in detecting small instances and handling extreme sparsity.Project page: https://pointlam.github.io/.