  Recently, Transformer has achieved great success in computer vision. However,
it is constrained because the spatial and temporal complexity grows
quadratically with the number of large points in 3D object detection
applications. Previous point-wise methods are suffering from time consumption
and limited receptive fields to capture information among points. In this
paper, we propose a two-stage hyperbolic cosine transformer (ChTR3D) for 3D
object detection from LiDAR point clouds. The proposed ChTR3D refines proposals
by applying cosh-attention in linear computation complexity to encode rich
contextual relationships among points. The cosh-attention module reduces the
space and time complexity of the attention operation. The traditional softmax
operation is replaced by non-negative ReLU activation and
hyperbolic-cosine-based operator with re-weighting mechanism. Extensive
experiments on the widely used KITTI dataset demonstrate that, compared with
vanilla attention, the cosh-attention significantly improves the inference
speed with competitive performance. Experiment results show that, among
two-stage state-of-the-art methods using point-level features, the proposed
ChTR3D is the fastest one.
