7 papers with code • 1 benchmarks • 0 datasets
Depth estimation using stereo cameras and a LiDAR sensor.
It is thus necessary to complete the sparse LiDAR data, where a synchronized guidance RGB image is often used to facilitate this completion.
The complementary characteristics of active and passive depth sensing techniques motivate the fusion of the Li-DAR sensor and stereo camera for improved depth perception.
Recent sparse depth completion for lidars only focuses on the lower scenes and produces irregular estimations on the upper because existing datasets, such as KITTI, do not provide groundtruth for upper areas.
The ability to detect objects, under image corruptions and different weather conditions is vital for deep learning models especially when applied to real-world applications such as autonomous driving.