Deep end-to-end 3D person detection from Camera and Lidar
M. Roth (TU Delft - Intelligent Vehicles, Daimler AG)
Dominik Jargot (Student TU Delft)
Dariu Gavrila (TU Delft - Intelligent Vehicles)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
We present a method for 3D person detection from camera images and lidar point clouds in automotive scenes. The method comprises a deep neural network which estimates the 3D location and extent of persons present in the scene. 3D anchor proposals are refined in two stages: a region proposal network and a subsequent detection network.For both input modalities high-level feature representations are learned from raw sensor data instead of being manually designed. To that end, we use Voxel Feature Encoders [1] to obtain point cloud features instead of widely used projection-based point cloud representations, thus allowing the network to learn to predict the location and extent of persons in an end-to-end manner.Experiments on the validation set of the KITTI 3D object detection benchmark [2] show that the proposed method outperforms state-of-the-art methods with an average precision (AP) of 47.06% on moderate difficulty.