ROFusion: Efficient Object Detection using Hybrid Point-wise
  Radar-Optical Fusion
Liu Liu Shuaifeng Zhi Zhenhua Du Li Liu Xinyu Zhang Kai Huo Weidong Jiang
Radars, due to their robustness to adverse weather conditions and ability to
measure object motions, have served in autonomous driving and intelligent
agents for years. However, Radar-based perception suffers from its unintuitive
sensing data, which lack of semantic and structural information of scenes. To
tackle this problem, camera and Radar sensor fusion has been investigated as a
trending strategy with low cost, high reliability and strong maintenance. While
most recent works explore how to explore Radar point clouds and images, rich
contextual information within Radar observation are discarded. In this paper,
we propose a hybrid point-wise Radar-Optical fusion approach for object
detection in autonomous driving scenarios. The framework benefits from dense
contextual information from both the range-doppler spectrum and images which
are integrated to learn a multi-modal feature representation. Furthermore, we
propose a novel local coordinate formulation, tackling the object detection
task in an object-centric coordinate. Extensive results show that with the
information gained from optical images, we could achieve leading performance in
object detection (97.69\% recall) compared to recent state-of-the-art methods
FFT-RadNet (82.86\% recall). Ablation studies verify the key design choices and
practicability of our approach given machine generated imperfect detections.
The code will be available at https://github.com/LiuLiu-55/ROFusion.