DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance

Understand

Depth estimation is an important capability for autonomous vehicles to understand and reconstruct 3D environments as well as avoid obstacles during the execution.

• Accurate depth sensors such as LiDARs are often heavy, expensive and can only provide sparse depth while lighter depth sensors such as stereo cameras are noiser in comparison.
• We propose an end-to-end learning algorithm that is capable of using sparse, noisy input depth for refinement and depth completion.
• Our model also produces the camera pose as a byproduct, making it a great solution for autonomous systems.