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

TL;DR

This paper introduces a neural implicit method for accurate 3D surface reconstruction from sparse, noisy radar data, effectively modeling scene geometry and view-dependent intensities for autonomous systems in low-visibility conditions.

Contribution

It presents a novel hybrid feature encoding approach that jointly models geometry and radar reflectance, improving surface reconstruction from radar point clouds.

Findings

Produces smoother, more accurate 3D surfaces than lidar-based methods

Effectively reconstructs view-dependent radar intensities

Performs better with sparser point clouds compared to traditional methods

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.