Pseudo-LiDAR-Based Road Detection
Libo Sun, Haokui Zhang, Wei Yin · IEEE Transactions on Circuits and Systems for Video Technology · 2022
Road detection is a critically important task for self-driving cars. By employing LiDAR data, recent works have significantly improved the accuracy of road detection. However, relying on LiDAR sensors limits the application of those methods when only cameras are available. In this paper, we propose a novel road detection approach with RGB images being the only input. Specifically, we exploit pseudo-LiDAR using depth estimation and propose a feature fusion network in which RGB images and learned depth information are fused for improved road detection. To optimize the network architecture and improve the efficiency of our network, we propose a method to search for the information propagation paths. Finally, to reduce the computational cost, we design a modality distillation strategy to avoid using depth estimation networks during inference. The resulting model eliminates the reliance on LiDAR sensors and achieves state-of-the-art performance on two challenging benchmarks, KITTI and R2D.