Researchers have achieved great success in dealing with 2D images using deep learning.
In recent years, 3D computer vision and geometry deep learning have gained ever more …

In the realm of digital creativity, our potential to craft intricate 3D worlds from imagination is
often hampered by the limitations of existing digital tools, which demand extensive expertise …

Implicit neural representations (INRs) have emerged as a powerful paradigm in scene
reconstruction and computer graphics, showcasing remarkable results. By utilizing neural …

This paper presents a new approach for 3D shape generation, enabling direct generative
modeling on a continuous implicit representation in wavelet domain. Specifically, we …

Reconstructing 3D shapes from single-view images has been a long-standing research
problem. In this paper, we present DISN, a Deep Implicit Surface Net-work which can …

Semantic reconstruction of indoor scenes refers to both scene understanding and object
reconstruction. Existing works either address one part of this problem or focus on …

Point set is a flexible and lightweight representation widely used for 3D deep learning.
However, their discrete nature prevents them from representing continuous and fine …

Point cloud completion aims at completing geometric and topological shapes from a partial
observation. However, some topology of the original shape is missing, existing methods …

Multi-view Stereo (MVS) with known camera parameters is essentially a 1D search problem
within a valid depth range. Recent deep learning-based MVS methods typically densely …

Abstract We present Neural Shape Deformation Priors, a novel method for shape
manipulation that predicts mesh deformations of non-rigid objects from user-provided …