Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometry Constrained Keypoints in Real-Time

Abstract

In this paper we propose a novel 3D single-shot object detection method for detecting vehicles in monocular RGB images. Our approach lifts 2D detections to 3D space by predicting additional regression and classification parameters and hence keeping the runtime close to pure 2D object detection. The additional parameters are transformed to 3D bounding box keypoints within the network under geometric constraints. Our proposed method features a full 3D description including all three angles of rotation without supervision by any labeled ground truth data for the object's orientation, as it focuses on certain keypoints within the image plane. While our approach can be combined with any modern object detection framework with only little computational overhead, we exemplify the extension of SSD for the prediction of 3D bounding boxes. We test our approach on different datasets for autonomous driving and evaluate it using the challenging KITTI 3D Object Detection as well as the novel nuScenes Object Detection benchmarks. While we achieve competitive results on both benchmarks we outperform current state-of-the-art methods in terms of speed with more than 20 FPS for all tested datasets and image resolutions.

SoK: Vehicle Orientation Representations for Deep Rotation Estimation

“…Shrivastava and Chakravarty [32] normalized orientation angle to (0,1). Carrillo and Waslander [33], Gahlert et al [34], and Plaut et al [35] studied the multiple degrees of freedom rotation of vehicle by applying regressing on scalar Euler angle independently on each axis.…”