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 …

Implicit neural representation has paved the way for new approaches to dynamic scene
reconstruction. Nonetheless cutting-edge dynamic neural rendering methods rely heavily on …

We present Co-SLAM, a neural RGB-D SLAM system based on a hybrid representation, that
performs robust camera tracking and high-fidelity surface reconstruction in real time. Co …

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 …

In this paper, we present a novel diffusion model called that generates multiview-consistent
images from a single-view image. Using pretrained large-scale 2D diffusion models, recent …

Abstract Neural Radiance Fields (NeRF) have constituted a remarkable breakthrough in
image-based 3D reconstruction. However, their implicit volumetric representations differ …

Neural radiance fields (NeRF) and its subsequent variants have led to remarkable progress
in neural rendering. While most of recent neural rendering works focus on objects and small …

We present a neural rendering-based method called NeRO for reconstructing the geometry
and the BRDF of reflective objects from multiview images captured in an unknown …

Abstract Neural Radiance Field (NeRF) has emerged as a powerful paradigm for scene
representation, offering high-fidelity renderings and reconstructions from a set of sparse and …

We propose a novel point-based representation, Gaussian surfels, to combine the
advantages of the flexible optimization procedure in 3D Gaussian points and the surface …