ObjectFusion: Multi-modal 3D object detection with object-centric fusion
Recent progress on multi-modal 3D object detection has featured BEV (Bird-Eye-View) based fusion, which effectively unifies both LiDAR point clouds and camera images in a shared BEV space. Nevertheless, it is not trivial to perform camera-to-BEV transformation due to the inherently ambiguous depth e...
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| Main Authors: | , , , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2023
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| Online Access: | https://ink.library.smu.edu.sg/sis_research/8306 https://ink.library.smu.edu.sg/context/sis_research/article/9309/viewcontent/Cai_ObjectFusion_Multi_modal_3D_Object_Detection_with_Object_Centric_Fusion_ICCV_2023_paper.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Recent progress on multi-modal 3D object detection has featured BEV (Bird-Eye-View) based fusion, which effectively unifies both LiDAR point clouds and camera images in a shared BEV space. Nevertheless, it is not trivial to perform camera-to-BEV transformation due to the inherently ambiguous depth estimation of each pixel, resulting in spatial misalignment between these two multi-modal features. Moreover, such transformation also inevitably leads to projection distortion of camera image features in BEV space. In this paper, we propose a novel Object-centric Fusion (ObjectFusion) paradigm, which completely gets rid of camera-to-BEV transformation during fusion to align object-centric features across different modalities for 3D object detection. ObjectFusion first learns three kinds of modality-specific feature maps (i.e., voxel, BEV, and image features) from LiDAR point clouds and its BEV projections, camera images. Then a set of 3D object proposals are produced from the BEV features via a heatmap-based proposal generator. Next, the 3D object proposals are reprojected back to voxel, BEV, and image spaces. We leverage voxel and RoI pooling to generate spatially aligned object-centric features for each modality. All the object-centric features of three modalities are further fused at object level, which is finally fed into the detection heads. Extensive experiments on nuScenes dataset demonstrate the superiority of our ObjectFusion, by achieving 69.8% mAP on nuScenes validation set and improving BEVFusion by 1.3%. |
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