UnDEMoN 2.0: Improved Depth and Ego Motion Estimation through Deep Image Sampling
This work addresses the challenge of accurate and efficient depth estimation for applications like autonomous driving, though it is incremental as it builds on an existing model.
The paper tackles the problem of depth and ego motion estimation from monocular images by improving the UnDEMoN model with a deep image sampling network combined with a bi-linear sampler, resulting in significant accuracy gains that outperform all existing state-of-the-art methods and reduce the number of tunable parameters.
In this paper, we provide an improved version of UnDEMoN model for depth and ego motion estimation from monocular images. The improvement is achieved by combining the standard bi-linear sampler with a deep network based image sampling model (DIS-NET) to provide better image reconstruction capabilities on which the depth estimation accuracy depends in un-supervised learning models. While DIS-NET provides higher order regression and larger input search space, the bi-linear sampler provides geometric constraints necessary for reducing the size of the solution space for an ill-posed problem of this kind. This combination is shown to provide significant improvement in depth and pose estimation accuracy outperforming all existing state-of-the-art methods in this category. In addition, the modified network uses far less number of tunable parameters making it one of the lightest deep network model for depth estimation. The proposed model is labeled as "UnDEMoN 2.0" indicating an improvement over the existing UnDEMoN model. The efficacy of the proposed model is demonstrated through rigorous experimental analysis on the standard KITTI dataset.