Eliminating Cross-modal Conflicts in BEV Space for LiDAR-Camera 3D
  Object Detection
Jiahui Fu Chen Gao Zitian Wang Lirong Yang Xiaofei Wang Beipeng Mu Si Liu
Recent 3D object detectors typically utilize multi-sensor data and unify
multi-modal features in the shared bird's-eye view (BEV) representation space.
However, our empirical findings indicate that previous methods have limitations
in generating fusion BEV features free from cross-modal conflicts. These
conflicts encompass extrinsic conflicts caused by BEV feature construction and
inherent conflicts stemming from heterogeneous sensor signals. Therefore, we
propose a novel Eliminating Conflicts Fusion (ECFusion) method to explicitly
eliminate the extrinsic/inherent conflicts in BEV space and produce improved
multi-modal BEV features. Specifically, we devise a Semantic-guided Flow-based
Alignment (SFA) module to resolve extrinsic conflicts via unifying spatial
distribution in BEV space before fusion. Moreover, we design a Dissolved Query
Recovering (DQR) mechanism to remedy inherent conflicts by preserving
objectness clues that are lost in the fusion BEV feature. In general, our
method maximizes the effective information utilization of each modality and
leverages inter-modal complementarity. Our method achieves state-of-the-art
performance in the highly competitive nuScenes 3D object detection dataset. The
code is released at https://github.com/fjhzhixi/ECFusion.