Hierarchical Binary Classification for Monocular Depth Estimation Extracting dense depth from a single image is an important and challenging task in computer vision. Recently, significant progress has been made in monocular depth estimation (MDE) by formulating this problem as a multilabel classification instead of traditional regression. However, to discretize depth into hundreds of labels results in complicate network output and a large number of parameters. In this paper, we propose to encode the discretized depth into a binary code and formulate MDE as a hierarchical binary classification (HBC) problem to reduce the complexity. Besides, by studying the distribution of individual bit of the encoded depth codes, we find that our encoding scheme can also solve the problem of depth data imbalance. We conduct experiments on KITTI and NYU Depth V2 datasets, which show that our simplified approach still achieves a comparable performance with stateoftheart methods.

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.