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Related Experiment Video

Updated: May 28, 2026

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LiDAR-Free 3D Auto-Labeling via Radar-Visual Spatio-Temporal Consistency.

Boning Zhu¹, Zhiqun Hu¹, Zhaoming Lu¹

¹Beijing Laboratory of Advanced Information Networks, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)

|May 27, 2026

Summary

This summary is machine-generated.

This study introduces a novel radar-visual auto-labeling framework for 3D annotation, overcoming limitations of vision foundation models. The method achieves significant improvements in bird's-eye-view and 3D intersection over union, enabling more accurate roadside scene understanding.

Keywords:

LiDAR-free 3D auto-labeling geometry refinement millimeter-wave radar radar–camera fusion roadside perception spatio-temporal consistency visual foundation model

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Area of Science:

Computer Vision
Robotics
Sensor Fusion

Background:

Vision foundation models (VFMs) generate 2D instance masks but struggle with 3D annotation due to scale ambiguity and noise.
Existing 3D auto-labeling methods often require expensive LiDAR sensors or lack physical plausibility in dynamic scenes.

Purpose of the Study:

To develop a LiDAR-free auto-labeling framework for 3D annotation using radar and visual data.
To enhance 3D geometry accuracy by leveraging cross-modal spatio-temporal consistency.

Main Methods:

Associating radar points, 2D masks, and pseudo-point clouds into object-centric sequences.
Employing an uncertainty-aware pose fusion module with automatically solved road priors.
Refining pseudo-point clouds by optimizing semantic landmarks from temporally consistent masks.

Main Results:

Achieved 49.1% bird's-eye-view (BEV) IoU and 43.0% 3D IoU on a real-world roadside dataset.
Outperformed a radar-camera fusion baseline by 5.5/5.9 points in BEV and 3D IoU.
Demonstrated the utility of generated pseudo-labels and semantic enhancement for downstream tasks.

Conclusions:

The proposed radar-visual framework effectively corrects 3D geometry for auto-labeling without LiDAR.
The method shows strong performance and potential for improving 3D annotation in dynamic roadside environments.
Further validation across diverse configurations is recommended for broader applicability.