C-LaV: Conditional Latent Velocity Field Denoising for Weather-Robust LiDAR Place Recognition

LiDAR-based place recognition is highly sensitive to rain, snow, and fog, where scattering and attenuation distort geometric structure and intensity. We tackle this problem with Conditional Latent Velocity Field (C-LaV) denoising, which restores weather-robust representations before retrieval. Single-sweep point clouds are projected into three-channel bird’s-eye-view (BEV) images and encoded with a frozen DINOv2-based BEV transformer to obtain a semantically anchored latent space shared across weather conditions. On this manifold, a conditional Flow Matching model learns a velocity field whose probability-flow ordinary differential equation (ODE) deterministically transports noisy latents toward their clear-weather counterparts. From the denoised manifold, a Sinkhorn Aggregation of Local Descriptors (SALAD) head produces compact global descriptors optimized with a truncated Smooth-AP loss. We also establish a unified adverse-weather benchmark with 3 m frame spacing and shared evaluation thresholds across KITTI, NCLT, and Boreas datasets. Under this protocol, C-LaV improves Recall@1 by 17.5% on NCLT snow and 21.5% on Boreas, achieving state-of-the-art weather robustness. Our dataset and code will be publicly available.