  Fusing data from cameras and LiDAR sensors is an essential technique to
achieve robust 3D object detection. One key challenge in camera-LiDAR fusion
involves mitigating the large domain gap between the two sensors in terms of
coordinates and data distribution when fusing their features. In this paper, we
propose a novel camera-LiDAR fusion architecture called, 3D Dual-Fusion, which
is designed to mitigate the gap between the feature representations of camera
and LiDAR data. The proposed method fuses the features of the camera-view and
3D voxel-view domain and models their interactions through deformable
attention. We redesign the transformer fusion encoder to aggregate the
information from the two domains. Two major changes include 1) dual query-based
deformable attention to fuse the dual-domain features interactively and 2) 3D
local self-attention to encode the voxel-domain queries prior to dual-query
decoding. The results of an experimental evaluation show that the proposed
camera-LiDAR fusion architecture achieved competitive performance on the KITTI
and nuScenes datasets, with state-of-the-art performances in some 3D object
detection benchmarks categories.
