  Edge computing-based 3D perception has received attention in intelligent
transportation systems (ITS) because real-time monitoring of traffic candidates
potentially strengthens Vehicle-to-Everything (V2X) orchestration. Thanks to
the capability of precisely measuring the depth information on surroundings
from LiDAR, the increasing studies focus on lidar-based 3D detection, which
significantly promotes the development of 3D perception. Few methods met the
real-time requirement of edge deployment because of high computation-intensive
operations. Moreover, an inconsistency problem of object detection remains
uncovered in the pointcloud domain due to large sparsity. This paper thoroughly
analyses this problem, comprehensively roused by recent works on determining
inconsistency problems in the image specialisation. Therefore, we proposed a 3D
harmonic loss function to relieve the pointcloud based inconsistent
predictions. Moreover, the feasibility of 3D harmonic loss is demonstrated from
a mathematical optimization perspective. The KITTI dataset and DAIR-V2X-I
dataset are used for simulations, and our proposed method considerably improves
the performance than benchmark models. Further, the simulative deployment on an
edge device (Jetson Xavier TX) validates our proposed model's efficiency.
