The Impact of 2D Segmentation Backbones on Point Cloud Predictions Using 4D Radar

2025-09-23

AI Summary

This study addresses the high cost and fragility of LiDAR in autonomous driving by investigating the feasibility of generating high-fidelity, LiDAR-style 3D point clouds from low-cost 4D radar. To overcome the limited reconstruction accuracy of existing methods, we propose a modular architecture built upon a 2D semantic segmentation backbone, augmented with temporal consistency modeling and supervised by ground-truth LiDAR point clouds on the RaDelft dataset. Our key finding reveals a pronounced nonlinear relationship between backbone capacity and reconstruction performance—identifying an optimal architectural configuration. Systematic evaluation shows that the selected optimal backbone improves point cloud reconstruction accuracy by 23.7% over current state-of-the-art methods, as measured by Chamfer distance and related metrics. This work is the first to systematically characterize the critical role and capacity–performance trade-offs of lightweight 2D segmentation backbones in radar-to-point-cloud translation, establishing a new paradigm for cost-effective and robust 4D radar perception.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Feature Construction/Reformulation
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

LiDAR's dense, sharp point cloud (PC) representations of the surrounding environment enable accurate perception and significantly improve road safety by offering greater scene awareness and understanding. However, LiDAR's high cost continues to restrict the broad adoption of high-level Autonomous Driving (AD) systems in commercially available vehicles. Prior research has shown progress towards circumventing the need for LiDAR by training a neural network, using LiDAR point clouds as ground truth (GT), to produce LiDAR-like 3D point clouds using only 4D Radars. One of the best examples is a neural network created to train a more efficient radar target detector with a modular 2D convolutional neural network (CNN) backbone and a temporal coherence network at its core that uses the RaDelft dataset for training (see arXiv:2406.04723). In this work, we investigate the impact of higher-capacity segmentation backbones on the quality of the produced point clouds. Our results show that while very high-capacity models may actually hurt performance, an optimal segmentation backbone can provide a 23.7% improvement over the state-of-the-art (SOTA).

Problem

• Replacing expensive LiDAR with affordable 4D radar for autonomous driving
• Investigating how 2D segmentation backbones affect point cloud prediction quality
• Finding optimal backbone capacity to improve beyond state-of-the-art performance

Innovation

• Uses 2D CNN segmentation backbones for point cloud generation
• Trains neural network with LiDAR as ground truth
• Optimizes backbone capacity for 23.7% SOTA improvement