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Found 5 papers, 1 papers with code
Neural Rendering based Urban Scene Reconstruction for Autonomous Driving
In particular, our method estimates dense and accurate 3D structures and creates an implicit map representation based on signed distance fields, which can be further rendered into RGB images, and depth maps.
Optical Flow for Autonomous Driving: Applications, Challenges and Improvements
Optical flow estimation is a well-studied topic for automated driving applications.
DytanVO: Joint Refinement of Visual Odometry and Motion Segmentation in Dynamic Environments
Learning-based visual odometry (VO) algorithms achieve remarkable performance on common static scenes, benefiting from high-capacity models and massive annotated data, but tend to fail in dynamic, populated environments.
Dynamic Dense RGB-D SLAM using Learning-based Visual Odometry
We propose a dense dynamic RGB-D SLAM pipeline based on a learning-based visual odometry, TartanVO.
Tailored Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud System
In this paper, we introduce KaiS, a learning-based scheduling framework for such edge-cloud systems to improve the long-term throughput rate of request processing.
