LiCAR: pseudo-RGB LiDAR image for CAR segmentation
This addresses car segmentation for autonomous driving by creating a new dataset from LiDAR, but it is incremental as it adapts existing methods to a new data format.
The paper tackles car segmentation by converting LiDAR data into pseudo-RGB images and applying instance segmentation neural networks, achieving 88% bounding box and 81.5% mask precision with YOLO-v8 large.
With the advancement of computing resources, an increasing number of Neural Networks (NNs) are appearing for image detection and segmentation appear. However, these methods usually accept as input a RGB 2D image. On the other side, Light Detection And Ranging (LiDAR) sensors with many layers provide images that are similar to those obtained from a traditional low resolution RGB camera. Following this principle, a new dataset for segmenting cars in pseudo-RGB images has been generated. This dataset combines the information given by the LiDAR sensor into a Spherical Range Image (SRI), concretely the reflectivity, near infrared and signal intensity 2D images. These images are then fed into instance segmentation NNs. These NNs segment the cars that appear in these images, having as result a Bounding Box (BB) and mask precision of 88% and 81.5% respectively with You Only Look Once (YOLO)-v8 large. By using this segmentation NN, some trackers have been applied so as to follow each car segmented instance along a video feed, having great performance in real world experiments.