A Conditional Adversarial Network for Scene Flow Estimation

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Authors Snehasis Mukherjee, Ravi Kumar Thakur
Journal/Conference Name 2019 28th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2019
Paper Category
Paper Abstract 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 large RGB-D benchmark sceneflow dataset.
Date of publication 2019
Code Programming Language Python

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