This paper reviews the NTIRE 2022 challenge on learning the super-Resolution space. This
challenge aims to raise awareness that the super-resolution problem is ill-posed. Since …

In this work, we explore the challenging task of generating 3D shapes from text. Beyond the
existing works, we propose a new approach for text-guided 3D shape generation, capable of …

This work attempts to provide a plausible theoretical framework that aims to interpret modern
deep (convolutional) networks from the principles of data compression and discriminative …

To learn intrinsic low-dimensional structures from high-dimensional data that most
discriminate between classes, we propose the principle of {\em Maximal Coding Rate …

Learning accurate and parsimonious point cloud representations of scene surfaces from
scratch remains a challenge in 3D representation learning. Existing point-based methods …

Neural radiance fields (NeRFs) have enabled high fidelity 3D reconstruction from multiple
2D input views. However, a well-known drawback of NeRFs is the less-than-ideal …

Recently, convolutional neural networks (CNNs) have achieved state-of-the-art performance
in infrared small target detection. However, the limited number of public training data …

While the community of 3D point cloud generation has witnessed a big growth in recent
years, there still lacks an effective way to enable intuitive user control in the generation …

This work proposes a new computational framework for learning a structured generative
model for real-world datasets. In particular, we propose to learn a C losed-loop Tr …

Shape completion is the problem of completing partial input shapes such as partial scans.
This problem finds important applications in computer vision and robotics due to issues such …