3D BAT: A SemiAutomatic, Webbased 3D Annotation Toolbox for FullSurround, MultiModal Data Streams In this paper, we focus on obtaining 2D and 3D labels, as well as track IDs for objects on the road with the help of a novel 3D Bounding Box Annotation Toolbox (3D BAT). Our open source, webbased 3D BAT incorporates several smart features to improve usability and efficiency. For instance, this annotation toolbox supports semiautomatic labeling of tracks using interpolation, which is vital for downstream tasks like tracking, motion planning and motion prediction. Moreover, annotations for all camera images are automatically obtained by projecting annotations from 3D space into the image domain. In addition to the raw image and point cloud feeds, a Masterview consisting of the top view (birdu0027seyeview), side view and front views is made available to observe objects of interest from different perspectives. Comparisons of our method with other publicly available annotation tools reveal that 3D annotations can be obtained faster and more efficiently by using our toolbox.

FANTrack: 3D MultiObject Tracking with Feature Association Network We propose a datadriven approach to online multiobject tracking (MOT) that uses a convolutional neural network (CNN) for data association in a trackingbydetection framework. The problem of multitarget tracking aims to assign noisy detections to apriori unknown and timevarying number of tracked objects across a sequence of frames. A majority of the existing solutions focus on either tediously designing cost functions or formulating the task of data association as a complex optimization problem that can be solved effectively. Instead, we exploit the power of deep learning to formulate the data association problem as inference in a CNN. To this end, we propose to learn a similarity function that combines cues from both image and spatial features of objects. Our solution learns to perform global assignments in 3D purely from data, handles noisy detections and a varying number of targets, and is easy to train. We evaluate our approach on the challenging KITTI dataset and show competitive results. Our code is available at this https URL.