  Monocular 3D object detection encounters occlusion problems in many
application scenarios, such as traffic monitoring, pedestrian monitoring, etc.,
which leads to serious false negative. Multi-view object detection effectively
solves this problem by combining data from different perspectives. However, due
to label confusion and feature confusion, the orientation estimation of
multi-view 3D object detection is intractable, which is important for object
tracking and intention prediction. In this paper, we propose a novel multi-view
3D object detection method named MVM3Det which simultaneously estimates the 3D
position and orientation of the object according to the multi-view monocular
information. The method consists of two parts: 1) Position proposal network,
which integrates the features from different perspectives into consistent
global features through feature orthogonal transformation to estimate the
position. 2) Multi-branch orientation estimation network, which introduces
feature perspective pooling to overcome the two confusion problems during the
orientation estimation. In addition, we present a first dataset for multi-view
3D object detection named MVM3D. Comparing with State-Of-The-Art (SOTA) methods
on our dataset and public dataset WildTrack, our method achieves very
competitive results.
