  3D object detection with multi-sensors is essential for an accurate and
reliable perception system of autonomous driving and robotics. Existing 3D
detectors significantly improve the accuracy by adopting a two-stage paradigm
which merely relies on LiDAR point clouds for 3D proposal refinement. Though
impressive, the sparsity of point clouds, especially for the points far away,
making it difficult for the LiDAR-only refinement module to accurately
recognize and locate objects.To address this problem, we propose a novel
multi-modality two-stage approach named FusionRCNN, which effectively and
efficiently fuses point clouds and camera images in the Regions of
Interest(RoI). FusionRCNN adaptively integrates both sparse geometry
information from LiDAR and dense texture information from camera in a unified
attention mechanism. Specifically, it first utilizes RoIPooling to obtain an
image set with a unified size and gets the point set by sampling raw points
within proposals in the RoI extraction step; then leverages an intra-modality
self-attention to enhance the domain-specific features, following by a
well-designed cross-attention to fuse the information from two
modalities.FusionRCNN is fundamentally plug-and-play and supports different
one-stage methods with almost no architectural changes. Extensive experiments
on KITTI and Waymo benchmarks demonstrate that our method significantly boosts
the performances of popular detectors.Remarkably, FusionRCNN significantly
improves the strong SECOND baseline by 6.14% mAP on Waymo, and outperforms
competing two-stage approaches. Code will be released soon at
https://github.com/xxlbigbrother/Fusion-RCNN.
