Parse geometry from a line: Monocular depth estimation with partial laser observation
By Yiyi Liao, Lichao Huang, Yue Wang, Sarath Kodagoda, Yinan Yu, Y. Liu. 2016
108 citations recorded by the source corpus.
Many standard robotic platforms are equipped with at least a fixed 2D laser range finder and a monocular camera. Although those platforms do not have sensors for 3D depth sensing capability, knowledge of depth is an essential part in many robotics activities. Therefore, recently, there is an increasing interest in depth estimation using monocular images. As this task is inherently ambiguous, the data-driven estimated depth might be unreliable in robotics applications. In this paper, we have attempted to improve the precision of monocular depth estimation by introducing 2D planar observation from the remaining laser range finder without extra cost. Specifically, we construct a dense reference map from the sparse laser range data, redefining the depth estimation task as estimating the distance between the real and the reference depth. To solve the problem, we construct a novel residual of residual neural network, and tightly combine the classification and regression losses for continuous depth estimation. Experimental results suggest that our method achieves considerable promotion compared to the state-of-the-art methods on both NYUD2 and KITTI, validating the effectiveness of our method on leveraging the additional sensory information. We further demonstrate the potential usage of our method in obstacle avoidance where our methodology provides comprehensive depth information compared to the solution using monocular camera or 2D laser range finder alone.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Media Technology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
In Computer Vision and Pattern Recognition
- Ruigang Yang
- Peng Wang
- Xinjing Cheng
- Sertac Karaman
- Fangchang Ma
- Lei Tai
- Luc Van Gool
- Marc Pollefeys
In Media Technology
- Sertac Karaman
- Fangchang Ma
- Peng Wang
- Ruigang Yang
- Xinjing Cheng
- Lei Tai
- Nantheera Anantrasirichai
- D. Bull
Citations in the corpus
- Learning Depth with Convolutional Spatial Propagation Network (2018, background): “Our strategy has several advantages over the previous stateof-the-art sparse-to-dense methods [9], [10], [71].”
- Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not (2016, methodology): “It showed that networks trained for semantic place categorization (Zhou et al., 2014; Liao et al., 2016b) were more effective than those trained for object detection (Girshick et al., 2014).”
- Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network (2018, background): “Practically, we show that the proposed strategy can also be easily extended to convert sparse depth samples to a dense depth map given corresponding image [15,13].”
- Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image (2017, methodology): “Our performance also compares favorably to other fusion techniques including [24, 25], and at the same time demands fewer samples.”