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

• URL: http://arxiv.org/abs/2502.06114v3
• Date: Fri, 23 May 2025 06:17:06 GMT
• Title: Enhanced 3D Object Detection via Diverse Feature Representations of 4D Radar Tensor
• Authors: Seung-Hyun Song, Dong-Hee Paek, Minh-Quan Dao, Ezio Malis, Seung-Hyun Kong,
• Abstract summary: Raw 4D Radar (4DRT) offers richer spatial and Doppler information than conventional point clouds.<n>We propose a novel 3D object detection framework that maximizes the utility of 4DRT while preserving efficiency.<n>We show 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.
• Score: 5.038148262901536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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