  In the perception task of autonomous driving, multi-modal methods have become
a trend due to the complementary characteristics of LiDAR point clouds and
image data. However, the performance of multi-modal methods is usually limited
by the sparsity of the point cloud or the noise problem caused by the
misalignment between LiDAR and the camera. To solve these two problems, we
present a new concept, Voxel Region (VR), which is obtained by projecting the
sparse local point clouds in each voxel dynamically. And we propose a novel
fusion method named Sparse-to-Dense Voxel Region Fusion (SDVRF). Specifically,
more pixels of the image feature map inside the VR are gathered to supplement
the voxel feature extracted from sparse points and achieve denser fusion.
Meanwhile, different from prior methods, which project the size-fixed grids,
our strategy of generating dynamic regions achieves better alignment and avoids
introducing too much background noise. Furthermore, we propose a multi-scale
fusion framework to extract more contextual information and capture the
features of objects of different sizes. Experiments on the KITTI dataset show
that our method improves the performance of different baselines, especially on
classes of small size, including Pedestrian and Cyclist.
