  Object detection in 3D with stereo cameras is an important problem in
computer vision, and is particularly crucial in low-cost autonomous mobile
robots without LiDARs.
  Nowadays, most of the best-performing frameworks for stereo 3D object
detection are based on dense depth reconstruction from disparity estimation,
making them extremely computationally expensive.
  To enable real-world deployments of vision detection with binocular images,
we take a step back to gain insights from 2D image-based detection frameworks
and enhance them with stereo features.
  We incorporate knowledge and the inference structure from real-time one-stage
2D/3D object detector and introduce a light-weight stereo matching module.
  Our proposed framework, YOLOStereo3D, is trained on one single GPU and runs
at more than ten fps. It demonstrates performance comparable to
state-of-the-art stereo 3D detection frameworks without usage of LiDAR data.
The code will be published in https://github.com/Owen-Liuyuxuan/visualDet3D.
