# CrossFusion: Interleaving Cross-modal Complementation for Noise-resistant 3D Object Detection Source: [CrossFusion: Interleaving Cross-modal Complementation for Noise-resistant 3D Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:12:38.410Z Authors: Yang Yang, Weijie Ma, Hao Chen, Linlin Ou, Xinyi Yu Published: 2023-04-19T14:35:16.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()