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Guided depth map super-resolution (GDSR) is one of the mainstream methods in depth map super-resolution, as high-resolution color images can guide the reconstruction of the depth maps and are often easy to obtain. However, how to make full use of extracted guidance information of the color image to improve the depth map reconstruction remains a challenging problem. In this paper, we first design a multi-scale feedback module (MF) that extracts multi-scale features and alleviates the information loss in network propagation. We further propose a novel multi-scale feedback network (MSF-Net) for guided depth map super-resolution, which can better extract and refine the features by sequentially joining MF blocks. Specifically, our MF block uses parallel sampling layers and feedback links between multiple time steps to better learn information at different scales. Moreover, an inter-scale attention module (IA) is proposed to adaptively select and fuse important features at different scales. Meanwhile, depth features and corresponding color features are interacted using cross-domain attention conciliation module (CAC) after each MF block. We evaluate the performance of our proposed method on both synthetic and real captured datasets. Extensive experimental results validate that the proposed method achieves state-of-the-art performance in both objective and subjective quality.

View synthesis aims to learn a view transformation and synthesize the target views from a single or multiple source views. Although previous view synthesis methods have obtained promising performance, they heavily rely on the supervision of the target view. In this paper, we propose an unsupervised single-view synthesis network (USVS-Net) to learn the view transformation without the supervision of the target view. Specifically, with the usage of only a single source view, a style-guidance view synthesis model is proposed to learn an intrinsic representation, which intends to describe the object from a reference pose. With the intrinsic representation, the view transformation is learned to boost the learning of the unsupervised single-view synthesis. Then, taking the style-guidance view synthesis model as the teacher, a prior-distillation view synthesis model is further presented as the student to learn a more direct view transformation. By utilizing the proposed method, high-quality target views are synthesized in a time-efficient manner. Experiments on both synthetic and real-scene datasets show that despite the lack of supervision of the target view, the proposed method achieves promising results compared with the existing view synthesis methods.