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 …

Considering the ill-posed nature, contrastive regularization has been developed for single
image dehazing, introducing the information from negative images as a lower bound …

We introduce DMTet, a deep 3D conditional generative model that can synthesize high-
resolution 3D shapes using simple user guides such as coarse voxels. It marries the merits …

Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …

Neural signed distance functions (SDFs) are emerging as an effective representation for 3D
shapes. State-of-the-art methods typically encode the SDF with a large, fixed-size neural …

We present a novel type of neural fields that uses general radial bases for signal
representation. State-of-the-art neural fields typically rely on grid-based representations for …

With the rising industrial attention to 3D virtual modeling technology, generating novel 3D
content based on specified conditions (eg text) has become a hot issue. In this paper, we …

Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …

Abstract Neural Radiance Fields (NeRFs) have achieved great success in the past few
years. However, most current methods still require intensive resources due to ray marching …

Most existing distillation methods ignore the flexible role of the temperature in the loss
function and fix it as a hyper-parameter that can be decided by an inefficient grid search. In …