Real-time monocular depth estimation with extremely light-weight neural network

Mian Jhong Chiu, Wei-Chen Chiu, Hua Tsung Chen, Jen-Hui Chuang

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

Obstacle avoidance and environment sensing are crucial applications in autonomous driving and robotics. Among all types of sensors, RGB camera is widely used in these applications as it can offer rich visual contents with relatively low-cost, and using a single image to perform depth estimation has become one of the main focuses in resent research works. However, prior works usually rely on highly complicated computation and power-consuming GPU to achieve such task; therefore, we focus on developing a real-time light-weight system for depth prediction in this paper. Based on the well-known encoder-decoder architecture, we propose a supervised learning-based CNN with detachable decoders that produce depth predictions with different scales. We also formulate a novel log-depth loss function that computes the difference of predicted depth map and ground truth depth map in log space, so as to increase the prediction accuracy for nearby locations. To train our model efficiently, we generate depth map and semantic segmentation with complex teacher models. Via a series of ablation studies and experiments, it is validated that our model can efficiently performs real-time depth prediction with only 0.32M parameters, with the best trained model outperforms previous works on KITTI dataset for various evaluation matrices.

原文English
主出版物標題Proceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
發行者Institute of Electrical and Electronics Engineers Inc.
頁面7050-7057
頁數8
ISBN(電子)9781728188089
DOIs
出版狀態Published - 2020
事件25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy
持續時間: 10 1月 202115 1月 2021

出版系列

名字Proceedings - International Conference on Pattern Recognition
ISSN(列印)1051-4651

Conference

Conference25th International Conference on Pattern Recognition, ICPR 2020
國家/地區Italy
城市Virtual, Milan
期間10/01/2115/01/21

指紋

深入研究「Real-time monocular depth estimation with extremely light-weight neural network」主題。共同形成了獨特的指紋。

引用此