  LiDAR-camera fusion methods have shown impressive performance in 3D object
detection. Recent advanced multi-modal methods mainly perform global fusion,
where image features and point cloud features are fused across the whole scene.
Such practice lacks fine-grained region-level information, yielding suboptimal
fusion performance. In this paper, we present the novel Local-to-Global fusion
network (LoGoNet), which performs LiDAR-camera fusion at both local and global
levels. Concretely, the Global Fusion (GoF) of LoGoNet is built upon previous
literature, while we exclusively use point centroids to more precisely
represent the position of voxel features, thus achieving better cross-modal
alignment. As to the Local Fusion (LoF), we first divide each proposal into
uniform grids and then project these grid centers to the images. The image
features around the projected grid points are sampled to be fused with
position-decorated point cloud features, maximally utilizing the rich
contextual information around the proposals. The Feature Dynamic Aggregation
(FDA) module is further proposed to achieve information interaction between
these locally and globally fused features, thus producing more informative
multi-modal features. Extensive experiments on both Waymo Open Dataset (WOD)
and KITTI datasets show that LoGoNet outperforms all state-of-the-art 3D
detection methods. Notably, LoGoNet ranks 1st on Waymo 3D object detection
leaderboard and obtains 81.02 mAPH (L2) detection performance. It is noteworthy
that, for the first time, the detection performance on three classes surpasses
80 APH (L2) simultaneously. Code will be available at
\url{https://github.com/sankin97/LoGoNet}.
