Neural rendering techniques combining machine learning with geometric reasoning have
arisen as one of the most promising approaches for synthesizing novel views of a scene …

Very recently neural implicit rendering techniques have been rapidly evolved and shown
great advantages in novel view synthesis and 3D scene reconstruction. However, existing …

With the advent of Neural Radiance Fields (NeRF), neural networks can now render novel
views of a 3D scene with quality that fools the human eye. Yet, generating these images is …

In this article, we perform a review of the state-of-the-art of hybrid machine learning in
medical imaging. We start with a short summary of the general developments of the past in …

Deep Neural Networks (DNNs) have shown impressive performance in computer vision
tasks; however, their vulnerability to adversarial attacks raises concerns regarding their …

Generative AI (AIGC, aka AI generated content) has made remarkable progress in the past
few years, among which text-guided content generation is the most practical one since it …

We present BlockGAN, an image generative model that learns object-aware 3D scene
representations directly from unlabelled 2D images. Current work on scene representation …

DIVeR builds on the key ideas of NeRF and its variants--density models and volume
rendering--to learn 3D object models that can be rendered realistically from small numbers …

We present a method for composing photorealistic scenes from captured images of objects.
Our work builds upon neural radiance fields (NeRFs), which implicitly model the volumetric …

Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision,
their vulnerability to adversarial attacks remains a critical concern. Extensive research has …