Enhanced 3D Object Detection via Diverse Feature Representations of 4D Radar Tensor

TL;DR

This paper introduces a scalable and efficient 3D object detection framework that leverages diverse features from 4D Radar Tensor through multi-teacher knowledge distillation, significantly improving detection accuracy on sparse radar data.

Contribution

It proposes a novel multi-teacher knowledge distillation approach that fuses diverse 4DRT features into a lightweight model for efficient 3D detection.

Findings

Achieves 7.3% AP_3D improvement over baseline on sparse inputs.

Reduces input data size by approximately 90 times.

Maintains competitive performance with denser-input models.

Abstract

Recent advances in automotive four-dimensional (4D) Radar have enabled access to raw 4D Radar Tensor (4DRT), offering richer spatial and Doppler information than conventional point clouds. While most existing methods rely on heavily pre-processed, sparse Radar data, recent attempts to leverage raw 4DRT face high computational costs and limited scalability. To address these limitations, we propose a novel three-dimensional (3D) object detection framework that maximizes the utility of 4DRT while preserving efficiency. Our method introduces a multi-teacher knowledge distillation (KD), where multiple teacher models are trained on point clouds derived from diverse 4DRT pre-processing techniques, each capturing complementary signal characteristics. These teacher representations are fused via a dedicated aggregation module and distilled into a lightweight student model that operates solely on a sparse Radar input. Experimental results on the K-Radar dataset demonstrate that our framework achieves improvements of 7.3% in AP_3D and 9.5% in AP_BEV over the baseline RTNH model when using extremely sparse inputs. Furthermore, it attains comparable performance to denser-input baselines while significantly reducing the input data size by about 90 times, confirming the scalability and efficiency of our approach.