[CS482] 3D Gaussian Splatting for Real-Time Radiance Field Rendering Page 1 3D Gaussian
Splatting for Real-Time Radiance Field Rendering November 20, 2023 Bernhard Kerbl …

We propose pixelNeRF, a learning framework that predicts a continuous neural scene
representation conditioned on one or few input images. The existing approach for …

In this work, we investigate the problem of creating high-fidelity 3D content from only a single
image. This is inherently challenging: it essentially involves estimating the underlying 3D …

We present MVSNeRF, a novel neural rendering approach that can efficiently reconstruct
neural radiance fields for view synthesis. Unlike prior works on neural radiance fields that …

We propose SparseFusion, a sparse view 3D reconstruction approach that unifies recent
advances in neural rendering and probabilistic image generation. Existing approaches …

Recent deep learning based approaches have shown promising results for the challenging
task of inpainting large missing regions in an image. These methods can generate visually …

We present an unsupervised learning framework for the task of dense 3D geometry and
camera motion estimation from unstructured video sequences. In common with recent work …

We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical
image registration. Traditional registration methods optimize an objective function for each …

Learning based methods have shown very promising results for the task of depth estimation
in single images. However, most existing approaches treat depth prediction as a supervised …

Abstract We introduce Pixel-aligned Implicit Function (PIFu), an implicit representation that
locally aligns pixels of 2D images with the global context of their corresponding 3D object …