The KITTI Vision Benchmark Suite

we propose a
dual-channel transformer-based feature encoding
network, which
integrates both set abstraction layer and
transformer block as
backbone. It enables the model to exploit fine-
grained as well
as long-range contextual information of objects,
thus providing
complementary relationship of two methods. In
addition, a
centroid estimation module is introduced to obtain
powerful
representation of the whole object. Finally,
considering the
significance of point density, which is crucial
for detection
performance, we propose a central density-aware
enhancement
module to equip center features with distinct
density features

TBD