  Multiview detection uses multiple calibrated cameras with overlapping fields
of views to locate occluded pedestrians. In this field, existing methods
typically adopt a ``human modeling - aggregation'' strategy. To find robust
pedestrian representations, some intuitively incorporate 2D perception results
from each frame, while others use entire frame features projected to the ground
plane. However, the former does not consider the human appearance and leads to
many ambiguities, and the latter suffers from projection errors due to the lack
of accurate height of the human torso and head. In this paper, we propose a new
pedestrian representation scheme based on human point clouds modeling.
Specifically, using ray tracing for holistic human depth estimation, we model
pedestrians as upright, thin cardboard point clouds on the ground. Then, we
aggregate the point clouds of the pedestrian cardboard across multiple views
for a final decision. Compared with existing representations, the proposed
method explicitly leverages human appearance and reduces projection errors
significantly by relatively accurate height estimation. On four standard
evaluation benchmarks, the proposed method achieves very competitive results.
Our code and data will be released at https://github.com/ZichengDuan/MvCHM.
