DH-Fusion: Depth-Aware Hybrid Feature Fusion for Multimodal 3D Object Detection
Keywords: Depth Aware, Multi Modality, 3D Object Detection
Abstract: State-of-the-art LiDAR-camera 3D object detectors usually focus on feature fusion. However, they neglect the factor of depth while designing the fusion strategy. In this work, we for the first time point out that different modalities play different roles as depth varies via statistical analysis and visualization. Based on this finding, we propose a Depth-Aware Hybrid Feature Fusion (DH-Fusion) strategy that guides the weights of point cloud and RGB image modalities by introducing depth encoding at both global and local levels. Specifically, the Depth-Aware Global Feature Fusion (DGF) module adaptively adjusts the weights of image Bird's-Eye-View (BEV) features in multi-modal global features via depth encoding. Furthermore, to compensate for the information lost when transferring raw features to the BEV space, we propose a Depth-Aware Local Feature Fusion (DLF) module, which adaptively adjusts the weights of original voxel features and multi-view image features in multi-modal local features via depth encoding. Extensive experiments on the nuScenes and KITTI datasets demonstrate that our DH-Fusion method surpasses previous state-of-the-art methods. Moreover, our DH-Fusion is more robust to various kinds of corruptions, outperforming previous methods on nuScenes-C w.r.t. both NDS and mAP.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 5429
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