The problem of Scene flow estimation in depth videos has been attracting attention of researchers of robot vision, due to its potential application in various areas of robotics. The conventional scene flow methods are difficult to use in reallife applications due to their long computational overhead. We propose a conditional adversarial network SceneFlowGAN for scene flow estimation. The proposed SceneFlowGAN uses loss function at two ends: both generator and descriptor ends. The proposed network is the first attempt to estimate scene flow using generative adversarial networks, and is able to estimate both the optical flow and disparity from the input stereo images simultaneously. The proposed method is experimented on a huge RGB-D benchmark sceneflow dataset.
In the field of human-robot interaction, teaching learning agents from human demonstrations via supervised learning has been widely studied and successfully applied to multiple domains such as self-driving cars and robot manipulation. However, the majority of the work on learning from human demonstrations utilizes only behavioral information from the demonstrator, i.e. what actions were taken, and ignores other useful information. In particular, eye gaze information can give valuable insight towards where the demonstrator is allocating their visual attention, and leveraging such information has the potential to improve agent performance. Previous approaches have only studied the utilization of attention in simple, synchronous environments, limiting their applicability to real-world domains. This work proposes a novel imitation learning architecture to learn concurrently from human action demonstration and eye tracking data to solve tasks where human gaze information provides important context. The proposed method is applied to a visual navigation task, in which an unmanned quadrotor is trained to search for and navigate to a target vehicle in a real-world, photorealistic simulated environment. When compared to a baseline imitation learning architecture, results show that the proposed gaze augmented imitation learning model is able to learn policies that achieve significantly higher task completion rates, with more efficient paths, while simultaneously learning to predict human visual attention. This research aims to highlight the importance of multimodal learning of visual attention information from additional human input modalities and encourages the community to adopt them when training agents from human demonstrations to perform visuomotor tasks.
Estimating scene flow in RGB-D videos is attracting much interest of the computer vision researchers, due to its potential applications in robotics. The state-ofthe-art techniques for scene flow estimation, typically rely on the knowledge of scene structure of the frame and the correspondence between frames. However, with the increasing amount of RGB-D data captured from sophisticated sensors like Microsoft Kinect, and the recent advances in the area of sophisticated deep learning techniques, introduction of an efficient deep learning technique for scene flow estimation, is becoming important. This paper introduces a first effort to apply a deep learning method for direct estimation of scene flow by presenting a fully convolutional neural network with an encoder-decoder (ED) architecture. The proposed network SceneEDNet involves estimation of three dimensional motion vectors of all the scene points from sequence of stereo images. The training for direct estimation of scene flow is done using consecutive pairs of stereo images and corresponding scene flow ground truth. The proposed architecture is applied on a huge dataset and provides meaningful results.
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