Recently, the emergence of diffusion models has opened up new opportunities for single-
view reconstruction. However, all the existing methods represent the target object as a …

Neural implicit surfaces are a promising tool for geometry processing that represent a solid
object as the zero level set of a neural network. Usually trained to approximate a signed …

We introduce GEM3D 1–a new deep, topology-aware generative model of 3D shapes. The
key ingredient of our method is a neural skeleton-based representation encoding …

Surface reconstruction has traditionally relied on the Multi-View Stereo (MVS)-based
pipeline, which often suffers from noisy and incomplete geometry. This is due to that …

Methods that use neural networks for synthesizing 3D shapes in the form of a part-based
representation have been introduced over the last few years. These methods represent …

This paper introduces a novel method for reconstructing meshes from sparse point clouds by
predicting edge connection. Existing implicit methods usually produce superior smooth and …

This paper presents a novel latent 3D diffusion model for the generation of neural voxel
fields, aiming to achieve accurate part-aware structures. Compared to existing methods …