arXiv Machine Learning
Sep 10

Heat Field Signatures: From Point Clouds to Smooth Geometry

Heat Field Signatures (HFS) lift irregular point clouds into a multiscale family of smooth ambient heat fields, enabling closed‑form computation of global and local geometric signatures directly from pairwise distances. HFS captures heat concentration, intrinsic dimension, anisotropy, and scale transitions, and introduces the Heat Dimension Spectrum (HDS) as a compact multiscale summary. The method serves as a descriptor, lightweight learned representation, or feature channel for neural point‑cloud models, outperforming strong baselines on synthetic and real‑world benchmarks while reducing end‑to‑end cost.

By Yuanqing Wang, Yapeng Tian, Baris Coskunuzer
arXiv Computer Vision
Sep 21

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

PointLAM introduces a new point-based 3D object detection architecture that addresses efficiency and fidelity trade-offs inherent in LiDAR point cloud processing. It employs a Laplacian Point Sampler (LPS) to accelerate downsampling while preserving foreground structure, and a Local Hadamard Aggregator (LHA) that replaces costly continuous interactions with a topology‑aware gating mechanism. Combined with Bi‑Directional Mamba layers, the resulting Local Attentive Mamba (LAM) block delivers competitive performance on nuScenes and Waymo datasets, outperforming voxel‑based competitors in detecting small objects and handling extreme sparsity with a smaller computational footprint.

By Xuanming Shang, Weijia Zhang, Chao Ma