Deep feature fusion for self-supervised monocular depth prediction
This work addresses depth estimation for autonomous driving and robotics, but it is incremental as it builds on existing self-supervised methods with a focus on multi-scale feature integration.
The paper tackles the problem of monocular depth prediction by proposing a deep feature fusion method that leverages multi-scale structures in images, achieving better or comparable results on the KITTI dataset.
Recent advances in end-to-end unsupervised learning has significantly improved the performance of monocular depth prediction and alleviated the requirement of ground truth depth. Although a plethora of work has been done in enforcing various structural constraints by incorporating multiple losses utilising smoothness, left-right consistency, regularisation and matching surface normals, a few of them take into consideration multi-scale structures present in real world images. Most works utilise a VGG16 or ResNet50 model pre-trained on ImageNet weights for predicting depth. We propose a deep feature fusion method utilising features at multiple scales for learning self-supervised depth from scratch. Our fusion network selects features from both upper and lower levels at every level in the encoder network, thereby creating multiple feature pyramid sub-networks that are fed to the decoder after applying the CoordConv solution. We also propose a refinement module learning higher scale residual depth from a combination of higher level deep features and lower level residual depth using a pixel shuffling framework that super-resolves lower level residual depth. We select the KITTI dataset for evaluation and show that our proposed architecture can produce better or comparable results in depth prediction.