This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …

Recent research has found that many families of machine learning models are vulnerable to
adversarial examples: inputs that are specifically designed to cause the target model to …

[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 …

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

We present Tensor4D, an efficient yet effective approach to dynamic scene modeling. The
key of our solution is an efficient 4D tensor decomposition method so that the dynamic scene …

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

Prevailing video frame interpolation algorithms, that generate the intermediate frames from
consecutive inputs, typically rely on complex model architectures with heavy parameters or …

Novel view synthesis from a single image has been a cornerstone problem for many Virtual
Reality applications that provide immersive experiences. However, most existing techniques …