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Found 6 papers, 3 papers with code
Weakly-Supervised Road Affordances Inference and Learning in Scenes without Traffic Signs
Road attributes understanding is extensively researched to support vehicle's action for autonomous driving, whereas current works mainly focus on urban road nets and rely much on traffic signs.
Robotics
Understanding Human Context in 3D Scenes by Learning Spatial Affordances with Virtual Skeleton Models
Robots are often required to operate in environments where humans are not present, but yet require the human context information for better human-robot interaction.
Towards navigation without precise localization: Weakly supervised learning of goal-directed navigation cost map
Autonomous navigation based on precise localization has been widely developed in both academic research and practical applications.
Robotics
Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser Observation
Many standard robotic platforms are equipped with at least a fixed 2D laser range finder and a monocular camera.
Understand Scene Categories by Objects: A Semantic Regularized Scene Classifier Using Convolutional Neural Networks
As scene images have larger diversity than the iconic object images, it is more challenging for deep learning methods to automatically learn features from scene images with less samples.
Place classification with a graph regularized deep neural network model
Furthermore, results show that the features automatically learned from the raw input range data can achieve competitive results to the features constructed based on statistical and geometrical information.
