Unsupervised confidence for LiDAR depth maps and applications
Depth perception is pivotal in many fields, such as robotics and autonomous driving, to name a few. Consequently, depth sensors such as LiDARs rapidly spread in many applications. The 3D point clouds generated by these sensors must often be coupled with an RGB camera to understand the framed scene semantically. Usually, the former is projected over the camera image plane, leading to a sparse depth map. Unfortunately, this process, coupled with the intrinsic issues affecting all the depth sensors, yields noise and gross outliers in the final output. Purposely, in this paper, we propose an effective unsupervised framework aimed at explicitly addressing this issue by learning to estimate the confidence of the LiDAR sparse depth map and thus allowing for filtering out the outliers. Experimental results on the KITTI dataset highlight that our framework excels for this purpose. Moreover, we demonstrate how this achievement can improve a wide range of tasks.
Code (1)
Tasks
Autonomous DrivingSimilar Papers 제목 키워드 기반
Uncertainty depth estimation with gated images for 3D reconstruction
Gated imaging is an emerging sensor technology for self-driving cars that provides high-contrast images even under adverse weather influence. It has been shown that this technology can even generate high-fidelity dense d…
3D ReconstructionDepth CompletionDepth EstimationSelf-Driving CarsSparse and noisy LiDAR completion with RGB guidance and uncertainty
This work proposes a new method to accurately complete sparse LiDAR maps guided by RGB images. For autonomous vehicles and robotics the use of LiDAR is indispensable in order to achieve precise depth predictions. A multi…
Autonomous VehiclesDepth CompletionDepth EstimationDepth PredictionSparse and noisy LiDAR completion with RGB guidance anduncertainty
his work proposes a new method to accurately complete sparse LiDAR maps guided by RGB images. For autonomous vehicles and robotics the use of LiDAR is indispensable in order to achieve precise depth predi…
Autonomous VehiclesDepth CompletionDepth EstimationDepth PredictionNoise-Aware Unsupervised Deep Lidar-Stereo Fusion
In this paper, we present LidarStereoNet, the first unsupervised Lidar-stereo fusion network, which can be trained in an end-to-end manner without the need of ground truth depth maps. By introducing a novel "Feedback Loo…
3D geometryDepth CompletionStereo MatchingStereo Matching HandDepth Completion from Sparse LiDAR Data with Depth-Normal Constraints
Depth completion aims to recover dense depth maps from sparse depth measurements. It is of increasing importance for autonomous driving and draws increasing attention from the vision community. Most of existing methods d…
Autonomous DrivingDecoderDepth Completion