Intensity-guided depth image estimation in long-range lidar

Abstract

Long-range lidar systems usually record large but extremely sparse data cubes. It is a great challenge to estimate accurate depth images of the photon-starved regime with fewer memory requirements and lower computational complexity. An intensity-guided method is introduced to estimate the depth image by using temporal-spatial correlation of the reflected signals. Multi-scale superpixels and fast time-domain windows are established in the preprocessing step, leading to smaller data cubes with reduced empty and noisy pixels. To strike a balance between smoothness and preserving sharp edges, the fast-converging alternating direction method of multipliers (ADMM) is used in the modified cost function to estimate depth images. Experimental results show that the proposed method yields better depth estimates than other state-of-the-art methods, especially for long-rang targets of field experiments with a low signal return level of ∼1 photon per pixel.