mlsanityData to 7 Oct 2026

CrossFusion: Interleaving Cross-modal Complementation for Noise-resistant 3D Object Detection

arXiv 2304.09694 ↗First posted 19 Apr 2023cs.CVcited by 2

Not on the map. Why?

Abstract (arXiv)

The combination of LiDAR and camera modalities is proven to be necessary and typical for 3D object detection according to recent studies. Existing fusion strategies tend to overly rely on the LiDAR modal in essence, which exploits the abundant semantics from the camera sensor insufficiently. However, existing methods cannot rely on information from other modalities because the corruption of LiDAR features results in a large domain gap. Following this, we propose CrossFusion, a more robust and noise-resistant scheme that makes full use of the camera and LiDAR features with the designed cross-modal complementation strategy. Extensive experiments we conducted show that our method not only outperforms the state-of-the-art methods under the setting without introducing an extra depth estimation network but also demonstrates our model's noise resistance without re-training for the specific malfunction scenarios by increasing 5.2% mAP and 2.4% NDS.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 41 of its 48 references that are in the map. First posted on arXiv 19 Apr 2023.

Builds on

41 of 48 references.

References · 41

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29 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.

2 papers.

  1. 2023Radars for Autonomous Driving: A Review of Deep Learning Methods and Challengescited by 10
  2. 2026Multi-Modal Decouple and Recouple Network for Robust 3D Object Detectioncited by 0