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LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection

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arxiv 2402.11735 v1 pith:TLYM2EJ2 submitted 2024-02-18 cs.RO cs.CV

Jingyu Song , Lingjun Zhao , Katherine A. Skinner This is my paper · ORCID
classification cs.ROcs.CV
keywords fusionfeaturelidar-radarlirafusiondetectionexistingmoduleobject
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We propose LiRaFusion to tackle LiDAR-radar fusion for 3D object detection to fill the performance gap of existing LiDAR-radar detectors. To improve the feature extraction capabilities from these two modalities, we design an early fusion module for joint voxel feature encoding, and a middle fusion module to adaptively fuse feature maps via a gated network. We perform extensive evaluation on nuScenes to demonstrate that LiRaFusion leverages the complementary information of LiDAR and radar effectively and achieves notable improvement over existing methods.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. V2X-R: Cooperative LiDAR-4D Radar Fusion with Denoising Diffusion for 3D Object Detection

    cs.CV 2024-11 conditional novelty 6.0 of 10

    The first simulated V2X dataset with 4D radar is introduced, and a radar-conditioned diffusion denoiser improves foggy and snowy 3D detection by up to 5.7-6.7 percentage points.

  2. MutualForce: Mutual-Aware Enhancement for 4D Radar-LiDAR 3D Object Detection

    cs.CV 2025-01 conditional novelty 5.0 of 10

    MutualForce, a mutual-aware radar-LiDAR fusion network, reports the best mAP on the View-of-Delft validation set by guiding feature learning with radar velocity and RCS and enriching radar BEV features with LiDAR shap...

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