Accurate Automatic 3D Annotation of Traffic Lights and Signs for Autonomous Driving
This addresses the need for large-scale training data for self-driving cars, though it is incremental as it builds on existing 2D detection methods.
The paper tackles the problem of generating accurate 3D annotations for traffic lights and signs in autonomous driving by introducing a method that uses only RGB images and GNSS/INS data, achieving effectiveness up to 200 meters.
3D detection of traffic management objects, such as traffic lights and road signs, is vital for self-driving cars, particularly for address-to-address navigation where vehicles encounter numerous intersections with these static objects. This paper introduces a novel method for automatically generating accurate and temporally consistent 3D bounding box annotations for traffic lights and signs, effective up to a range of 200 meters. These annotations are suitable for training real-time models used in self-driving cars, which need a large amount of training data. The proposed method relies only on RGB images with 2D bounding boxes of traffic management objects, which can be automatically obtained using an off-the-shelf image-space detector neural network, along with GNSS/INS data, eliminating the need for LiDAR point cloud data.