|
|
Akhil Gurram, Onay Urfalioglu, Ibrahim Halfaoui, Fahd Bouzaraa and Antonio Lopez. 2020. Semantic Monocular Depth Estimation Based on Artificial Intelligence. ITSM, 13(4), 99–103.
Abstract: Depth estimation provides essential information to perform autonomous driving and driver assistance. A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels where the same raw training data is associated with both types of ground truth, i.e., depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, i.e., that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on monocular depth estimation.
|
|
|
|
Akhil Gurram, Ahmet Faruk Tuna, Fengyi Shen, Onay Urfalioglu and Antonio Lopez. 2021. Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision. TITS, 23(8), 12738–12751.
Abstract: Depth information is essential for on-board perception in autonomous driving and driver assistance. Monocular depth estimation (MDE) is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground truth (GT). Usually, this GT is acquired at training time through a calibrated multi-modal suite of sensors. However, also using only a monocular system at training time is cheaper and more scalable. This is possible by relying on structure-from-motion (SfM) principles to generate self-supervision. Nevertheless, problems of camouflaged objects, visibility changes, static-camera intervals, textureless areas, and scale ambiguity, diminish the usefulness of such self-supervision. In this paper, we perform monocular depth estimation by virtual-world supervision (MonoDEVS) and real-world SfM self-supervision. We compensate the SfM self-supervision limitations by leveraging virtual-world images with accurate semantic and depth supervision and addressing the virtual-to-real domain gap. Our MonoDEVSNet outperforms previous MDE CNNs trained on monocular and even stereo sequences.
|
|
|
|
Adrien Gaidon, Antonio Lopez and Florent Perronnin. 2018. The Reasonable Effectiveness of Synthetic Visual Data. IJCV, 126(9), 899–901.
|
|
|
|
A.F. Sole, S. Ngan, G. Sapiro, X. Hu and Antonio Lopez. 2001. Anisotropic 2-D and 3-D Averaging of fMRI Signals. IEEE Transactions on Medical Imaging, 20(2): 86–93 (IF: 3.142).
|
|
|
|
A.F. Sole, Antonio Lopez and G. Sapiro. 2001. Crease Enhancement Diffusion. Computer Vision and Image Understanding, 84(2): 241–248 (IF: 1.298).
|
|
|
|
A. Restrepo, Angel Sappa and M. Devy. 2005. Edge registration versus triangular mesh registration, a comparative study.
|
|
|
|
A. Pujol, Jordi Vitria, Felipe Lumbreras and Juan J. Villanueva. 2001. Topological principal component analysis for face encoding and recognition. PRL, 22(6-7), 769–776.
|
|
