SD4R: Sparse-to-Dense Learning for 3D Object Detection with 4D Radar

2026-02-24

AI Summary

This work addresses the challenge of degraded 3D object detection performance caused by the inherent sparsity and noise in 4D radar point clouds, particularly under extremely sparse conditions where existing densification methods lack robustness. To overcome this limitation, the authors propose the SD4R framework, which first employs a Foreground Point Generator (FPG) to suppress noise and produce high-quality dense point clouds, followed by a Logit-Query Encoder (LQE) to enhance pillar-based feature representations. By innovatively integrating foreground-aware densification with query-driven feature encoding, the proposed method achieves state-of-the-art 3D detection performance on the View-of-Delft dataset, demonstrating its effectiveness in noise suppression and sparse point cloud reconstruction.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

4D radar measurements offer an affordable and weather-robust solution for 3D perception. However, the inherent sparsity and noise of radar point clouds present significant challenges for accurate 3D object detection, underscoring the need for effective and robust point clouds densification. Despite recent progress, existing densification methods often fail to address the extreme sparsity of 4D radar point clouds and exhibit limited robustness when processing scenes with a small number of points. In this paper, we propose SD4R, a novel framework that transforms sparse radar point clouds into dense representations. SD4R begins by utilizing a foreground point generator (FPG) to mitigate noise propagation and produce densified point clouds. Subsequently, a logit-query encoder (LQE) enhances conventional pillarization, resulting in robust feature representations. Through these innovations, our SD4R demonstrates strong capability in both noise reduction and foreground point densification. Extensive experiments conducted on the publicly available View-of-Delft dataset demonstrate that SD4R achieves state-of-the-art performance. Source code is available at https://github.com/lancelot0805/SD4R.

Problem

• 4D radar
• 3D object detection
• point cloud sparsity
• noise
• densification

Innovation

• 4D radar
• point cloud densification
• foreground point generation
• logit-query encoder
• 3D object detection