  The ability to accurately detect and localize objects is recognized as being
the most important for the perception of self-driving cars. From 2D to 3D
object detection, the most difficult is to determine the distance from the
ego-vehicle to objects. Expensive technology like LiDAR can provide a precise
and accurate depth information, so most studies have tended to focus on this
sensor showing a performance gap between LiDAR-based methods and camera-based
methods. Although many authors have investigated how to fuse LiDAR with RGB
cameras, as far as we know there are no studies to fuse LiDAR and stereo in a
deep neural network for the 3D object detection task. This paper presents
SLS-Fusion, a new approach to fuse data from 4-beam LiDAR and a stereo camera
via a neural network for depth estimation to achieve better dense depth maps
and thereby improves 3D object detection performance. Since 4-beam LiDAR is
cheaper than the well-known 64-beam LiDAR, this approach is also classified as
a low-cost sensors-based method. Through evaluation on the KITTI benchmark, it
is shown that the proposed method significantly improves depth estimation
performance compared to a baseline method. Also, when applying it to 3D object
detection, a new state of the art on low-cost sensor based method is achieved.
