Radar as Tokens: Learning Unified Radar-Native Representations

Millimeter-wave radar is an increasingly important sensing modality for robust perception under adverse weather (outdoor) and lighting conditions (indoor). However, learning transferable radar representations for diverse indoor/outdoor downstream tasks remains challenging because radar measurements are both noisy and geometrically non-uniform: multipath, sidelobes, and low angular resolution introduce ambiguous returns, while the polar-coordinate representation format induces range-dependent spatial resolution; that is, the further from the antenna, the larger the cross-range resolution cell. Existing radar perception pipelines often reuse LiDAR- or Cartesian-grid representations, leaving downstream models to resolve radar-specific noise and geometry implicitly through task supervision. We introduce (Radar as Tokens), referred to as RadTok, a radar-native, task-agnostic representation. RadTok is built on two complementary principles. First, it adopts task-agnostic discrete spatial tokenization, inspired by VQ-style representation learning, to compress radar tensors into compact tokens that suppress ubiquitous radar clutter while preserving consistent target structure. Second, it introduces radar-native operations through resolution-aware polar-grid resampling and a learned de-sharpener that models radar’s spatially varying degradation. The resulting representation is denoised, resolution-aware, and directly consumed by existing spatial-feature-based perception pipelines without any modification to their architectures. With a single tokenizer design, RadTok consistently improves radar perception across multiple downstream tasks, reducing indoor absolute pose error by 17.8% on RT-Pose and, on K-Radar, outperforming baseline methods by a margin of 10+ AP in outdoor 3D detection and 2.2 mIoU in occupancy grid prediction.