Deep feature fusion for self-supervised monocular depth prediction

• URL: http://arxiv.org/abs/2005.07922v1
• Date: Sat, 16 May 2020 09:42:36 GMT
• Title: Deep feature fusion for self-supervised monocular depth prediction
• Authors: Vinay Kaushik, Brejesh Lall
• Abstract summary: We propose a deep feature fusion method 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. We also propose a refinement module learning higher scale residual depth from a combination of higher level deep features and lower level residual depth.
• Score: 7.779007880126907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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