Improving 3D Object Detection for Pedestrians with Virtual MultiView Synthesis Orientation Estimation 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 MultiView Synthesis module that can be adopted into 3D object detection methods to improve orientation estimation. The module uses a multistep process to acquire the finegrained semantic information required for accurate orientation estimation. First, the scenexe2x80x99s 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 objectxe2x80x99s appearance. We show that this module greatly improves the orientation estimation on the challenging pedestrian class on the KITTI benchmark. When used with the opensource 3D detector AVODFPN, we outperform all other published methods on the pedestrian Orientation, 3D, and Birdxe2x80x99s Eye View benchmarks.

Cascaded Sliding Window Based RealTime 3D Region Proposal for Pedestrian Detection* Pedestrian detection is an indispensable technology for designing autonomous driving systems. This paper proposes a cascaded sliding window based realtime approach to generate 3D pedestrian proposals on point clouds only. After rasterizing the raw point cloud, a 3D sliding window is adopted to extract pedestrian candidates. Two features are proposed to improve the proposal performance. One is the central points density feature, which acts as a filter to speed up the process and reduce false alarms. The other is the location feature, including the density distribution and height difference distribution of the point cloud, which describes the profile and location of an object in a sliding window. The scores generated by this feature are then applied to nonmaximum suppression (NMS) to remove suboptimal boxes. Experiments on the KITTI 3D object detection benchmark show that our approach achieves stateoftheart results among comparable methods, while maintaining the speed and accuracy tradeoff.