Accurate Surface and Reflectance Modelling from 3D Radar Data with Neural Radiance Fields

2026-03-26

AI Summary

Radar point clouds are inherently sparse and noisy, posing significant challenges for robust and high-fidelity reconstruction of 3D surfaces and reflectance properties. This work introduces neural radiance fields to radar data processing for the first time, proposing an implicit representation based on signed distance fields (SDF) that jointly models scene geometry and view-dependent radar intensity. By employing a memory-efficient hybrid feature encoding, the method substantially outperforms conventional explicit approaches under sparse input conditions. It not only produces smoother and more accurate continuous surfaces but also enables high-fidelity reconstruction of view-dependent radar reflectance, demonstrating superior performance in both geometric fidelity and radiometric consistency.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Representation Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Robust scene representation is essential for autonomous systems to safely operate in challenging low-visibility environments. Radar has a clear advantage over cameras and lidars in these conditions due to its resilience to environmental factors such as fog, smoke, or dust. However, radar data is inherently sparse and noisy, making reliable 3D surface reconstruction challenging. To address these challenges, we propose a neural implicit approach for 3D mapping from radar point clouds, which jointly models scene geometry and view-dependent radar intensities. Our method leverages a memory-efficient hybrid feature encoding to learn a continuous Signed Distance Field (SDF) for surface reconstruction, while also capturing radar-specific reflective properties. We show that our approach produces smoother, more accurate 3D surface reconstructions compared to existing lidar-based reconstruction methods applied to radar data, and can reconstruct view-dependent radar intensities. We also show that in general, as input point clouds get sparser, neural implicit representations render more faithful surfaces, compared to traditional explicit SDFs and meshing techniques.

Problem

• radar
• 3D reconstruction
• surface modeling
• reflectance modeling
• neural radiance fields

Innovation

• Neural Radiance Fields
• Radar 3D Reconstruction
• Signed Distance Field
• View-dependent Reflectance
• Neural Implicit Representation