Neural Radiance Field (NeRF), a new novel view synthesis with implicit scene
representation has taken the field of Computer Vision by storm. As a novel view synthesis …

Non‐rigid registration computes an alignment between a source surface with a target
surface in a non‐rigid manner. In the past decade, with the advances in 3D sensing …

A diffusion model learns to predict a vector field of gradients. We propose to apply chain rule
on the learned gradients, and back-propagate the score of a diffusion model through the …

Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging
applications in computer vision, graphics, robotics, and more. In order to streamline the …

Neural surface reconstruction has been shown to be powerful for recovering dense 3D
surfaces via image-based neural rendering. However, current methods struggle to recover …

In recent years, neural implicit surface reconstruction methods have become popular for
multi-view 3D reconstruction. In contrast to traditional multi-view stereo methods, these …

Diffusion models have emerged as the state-of-the-art for image generation, among other
tasks. Here, we present an efficient diffusion-based model for 3D-aware generation of neural …

We present TensoRF, a novel approach to model and reconstruct radiance fields. Unlike
NeRF that purely uses MLPs, we model the radiance field of a scene as a 4D tensor, which …

We propose a method to allow precise and extremely fast mesh extraction from 3D Gaussian
Splatting. Gaussian Splatting has recently become very popular as it yields realistic …

Recent advances in modeling 3D objects mostly rely on synthetic datasets due to the lack of
large-scale real-scanned 3D databases. To facilitate the development of 3D perception …