Reconstruction of a continuous surface of two-dimensional manifold from its raw, discrete
point cloud observation is a long-standing problem in computer vision and graphics …

Neural radiance fields (NeRFs) refer to a suit of deep neural networks that are used to learn
and represent objects or scenes. Generally speaking, NeRFs have five main characters …

We present a novel method for reconstructing a 3D implicit surface from a large-scale,
sparse, and noisy point cloud. Our approach builds upon the recently introduced Neural …

We present ShapeFormer, a transformer-based network that produces a distribution of
object completions, conditioned on incomplete, and possibly noisy, point clouds. The …

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

Surface reconstruction for point clouds is an important task in 3D computer vision. Most of
the latest methods resolve this problem by learning signed distance functions (SDF) from …

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 …

We present SP-GAN, a new unsupervised sphere-guided generative model for direct
synthesis of 3D shapes in the form of point clouds. Compared with existing models, SP-GAN …

We present a StyleGAN2‐based deep learning approach for 3D shape generation, called
SDF‐StyleGAN, with the aim of reducing visual and geometric dissimilarity between …

Neural signed distance functions (SDFs) have shown remarkable capability in representing
geometry with details. However, without signed distance supervision, it is still a challenge to …