Lambertian Model-Based Normal Guided Depth Completion for LiDAR-Camera System

Abstract

Depth completion is an essential task for the dense scene reconstruction on light detection and ranging (LiDAR)-camera system. Learning-based method achieves precise depth completion results on specific data sets. However, for the general outdoor scenes with insufficient labeled data sets, an efficient nonlearning method is still required. In this letter, from the geometrical constraint between depth and normal, a novel nonlearning normal guided depth completion method is proposed. For the objects in the outdoor scene, local brightness normal (LBN) constraint is derived from the Lambertian model. It is used to recover dense normal from RGB image and sparse normal. After that, we present a pipeline for depth completion with the guidance of dense normal. Extensive experiments on the KITTI depth completion data set demonstrate that our method achieves smaller root mean squared error (RMSE) than current nonlearning methods.