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.

翻译：多视图探测使用多校准相机,其视野范围重叠,以定位隐蔽行人。在这一领域,现有方法通常采用“人造模型-聚合”战略。为了找到强健的行人代表,有些直觉地将每个框架的2D感知结果纳入每个框架,而另一些则使用向地面平面预测的整个框架特征。然而,前者不考虑人的外观,导致许多模糊不清,而后者则由于人类身体和头部的高度不准确,而存在预测错误。在本文中,我们提议以人点云模型为基础的新的行人代表制方案。具体地说,我们用光线追踪来进行整体人类深度估计。我们用光线追踪来模拟行人,在地面将行人作为直立、薄的纸板点云进行模拟。然后,我们将行人纸板的点云汇集到多个角度,以便作出最后决定。与现有的表达相比,拟议方法明确利用人表和预测错误,通过相对准确的高度估计大大降低。在四个标准评价基准上,拟议方法将产生非常具有竞争性的结果。我们的代码和数据将在https://github.com/ZchhengDuan/MHMH.