2021 IEEE International Conference on Image Processing
19-22 September 2021 • Anchorage, Alaska, USA
Imaging Without Borders
| Paper ID | MLR-APPL-IP-8.3 | ||
| Paper Title | GRAYSCALE AND NORMAL GUIDED DEPTH COMPLETION WITH A LOW-COST LIDAR | ||
| Authors | Qingyang Yu, Lei Chu, Qi Wu, Ling Pei, Shanghai Jiao Tong University, China | ||
| Session | MLR-APPL-IP-8: Machine learning for image processing 8 | ||
| Location | Area E | ||
| Session Time: | Wednesday, 22 September, 14:30 - 16:00 | ||
| Presentation Time: | Wednesday, 22 September, 14:30 - 16:00 | ||
| Presentation | Poster | ||
| Topic | Applications of Machine Learning: Machine learning for image processing | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | 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. | ||