Parse geometry from a line: Monocular depth estimation with partial laser observation

Abstract

Abstract-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.

DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion

“…We use this dataset to verify that our model is able learn in different environments using different sources of input data, since here a Kinect RGBD sensor is used to collect data in various common environments such as offices and homes. [13], [42]. Our approach performs comparably to the the approach of Ma et al and better than that of Liao et al .…”

Deep Non-Rigid Structure From Motion

“…A line is fitted based on the data. The left comes from subject 05, the middle from subject 18, the right from subject 64. reconstruction from sparse observations [9,24,21,22,7]. These two solutions make our central pipeline of DNN more easily to adapt to handling missing data.…”

Depth Coefficients for Depth Completion

“…Loss function for depth completion A key component of depth completion is the choice of loss function. Recent work has explored loss functions including L2 [4,19], L1 [20], inverse-L1 [15], and softmax losses on depth [16]. While these loss functions can achieve low error on measures including RMSE, MAE, iMAE, often it comes at the cost of smoothing out depth estimate at object boundaries.…”