  3D object detection is an important task that has been widely applied in
autonomous driving. Recently, fusing multi-modal inputs, i.e., LiDAR and camera
data, to perform this task has become a new trend. Existing methods, however,
either ignore the sparsity of Lidar features or fail to preserve the original
spatial structure of LiDAR and the semantic density of camera features
simultaneously due to the modality gap. To address issues, this letter proposes
a novel bidirectional complementary Lidar-camera fusion framework, called
BiCo-Fusion that can achieve robust semantic- and spatial-aware 3D object
detection. The key insight is to mutually fuse the multi-modal features to
enhance the semantics of LiDAR features and the spatial awareness of the camera
features and adaptatively select features from both modalities to build a
unified 3D representation. Specifically, we introduce Pre-Fusion consisting of
a Voxel Enhancement Module (VEM) to enhance the semantics of voxel features
from 2D camera features and Image Enhancement Module (IEM) to enhance the
spatial characteristics of camera features from 3D voxel features. Both VEM and
IEM are bidirectionally updated to effectively reduce the modality gap. We then
introduce Unified Fusion to adaptively weight to select features from the
enchanted Lidar and camera features to build a unified 3D representation.
Extensive experiments demonstrate the superiority of our BiCo-Fusion against
the prior arts. Project page: https://t-ys.github.io/BiCo-Fusion/.
