Title: PathFusion: Path-consistent Lidar-Camera Deep Feature Fusion. Abstract: Fusing camera with LiDAR is a promising technique to improve the accuracy of 3D detection due to its complementary physical properties.
While most existing methods focus on fusing camera features directly with raw LiDAR point clouds or shallow 3D features, it is observed that direct deep 3D feature fusion achieves inferior accuracy due to feature mis-alignment. The mis-alignment that originates from the feature aggregation across large receptive fields becomes increasingly severe for deep network stages. In this paper, we propose PathFusion to enable path-consistent LiDAR-camera deep feature fusion. PathFusion introduces a path consistency loss between shallow and deep features, which encourages the 2D backbone and its fusion path to transform 2D features in a way that is semantically aligned with the transform of the 3D backbone. We apply PathFusion to the prior-art fusion baseline, Focals Conv, and observe more than 1.2% mAP improvements on the nuScenes test split consistently with and without testing-time augmentations. Moreover, PathFusion also improves KITTI AP 3D (R11) by more than 0.6% on moderate level.