Monocular Depth Estimation in New Environments With Absolute Scale In this work we propose an unsupervised training method that finetunes a single image depth estimation CNN towards a new environment. The network, which has been pretrained on stereo data, only requires monocular input for finetuning. Unlike other unsupervised methods, it produces depth estimations with absolute scale xe2x80x93 a feature that is essential for most practical applications, yet has mostly been overlooked in the literature. First, we show how our method allows adapting a network trained on one dataset (Cityscapes) to another (KITTI). Next, by splitting KITTI in subsets, we show the sensitivity of pretrained models to a domain shift. We then demonstrate that, by finetuning the model using our method, it is possible to improve the performance on the target subset, without using stereo or any form of groundtruth depth and with preservation of the correct absolute scale.

Unsupervised Monocular Depth Estimation with Encoderdecoder Network * Although fully supervised learning methods could achieve good results for monocular depth estimation, it is very expensive to obtain ground truth depth data for training. In this paper, an encoderdecoder network is proposed to estimate monocular depth with unsupervised learning. During training, this network is able to enhance the weight of the effective features in the image by learning. Therefore, the encoder is able to extract more effective features and the decoder generates more targeted results. Compared to supervised learning, the proposed network is trained by stereo pairs and image reconstruction error, rather than ground truth depth. The proposed network achieves competitive results for monocular depth estimation on the KITTI driving dataset.