  Accurately estimating the orientation of pedestrians is an important and
challenging task for autonomous driving because this information is essential
for tracking and predicting pedestrian behavior. This paper presents a flexible
Virtual Multi-View Synthesis module that can be adopted into 3D object
detection methods to improve orientation estimation. The module uses a
multi-step process to acquire the fine-grained semantic information required
for accurate orientation estimation. First, the scene's point cloud is
densified using a structure preserving depth completion algorithm and each
point is colorized using its corresponding RGB pixel. Next, virtual cameras are
placed around each object in the densified point cloud to generate novel
viewpoints, which preserve the object's appearance. We show that this module
greatly improves the orientation estimation on the challenging pedestrian class
on the KITTI benchmark. When used with the open-source 3D detector AVOD-FPN, we
outperform all other published methods on the pedestrian Orientation, 3D, and
Bird's Eye View benchmarks.
