Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

Deep learning has become the most widely used approach for cardiac image segmentation
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …

Abstract We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an
object given just a single RGB image. To perform novel view synthesis in this …

Single image 3D reconstruction is an important but challenging task that requires extensive
knowledge of our natural world. Many existing methods solve this problem by optimizing a …

We propose SparseFusion, a sparse view 3D reconstruction approach that unifies recent
advances in neural rendering and probabilistic image generation. Existing approaches …

Recent advancements in 3D reconstruction from single images have been driven by the
evolution of generative models. Prominent among these are methods based on Score …

Point clouds captured in real-world applications are often incomplete due to the limited
sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point …

We introduce 3DShape2VecSet, a novel shape representation for neural fields designed for
generative diffusion models. Our shape representation can encode 3D shapes given as …

Traditional approaches for learning 3D object categories have been predominantly trained
and evaluated on synthetic datasets due to the unavailability of real 3D-annotated category …

Powerful priors allow us to perform inference with insufficient information. In this paper, we
propose an autoregressive prior for 3D shapes to solve multimodal 3D tasks such as shape …