In this paper, we introduce DenseLivox, a dataset with dense and accurate depth as ground truth. To our best knowledge, it is the first dataset with dense ground truth designed for LiDAR depth completion using a low-cost LiDAR. Also, we develop a simple yet effective multi-task learning network to tackle the problem of depth completion. Compared to the works in the literature, our model’s uniqueness is that it completes a depth map, a normal map, and a grayscale image simultaneously. To address the area with heavy noises, we use modified Huber loss to smooth these outliers’ effect. We evaluate our method on DenseLivox and show that accuracy is greatly improved with the grayscale and normal guidance. Our method outperforms other depth-only methods and is comparable to the methods that take RGB and depth as input.