Deep learning (DL) is being successfully applied across multiple domains, yet these models
learn in a most artificial way: they require large quantities of labeled data to grasp even …

Abstract We present Block-NeRF, a variant of Neural Radiance Fields that can represent
large-scale environments. Specifically, we demonstrate that when scaling NeRF to render …

Modeling and re-rendering dynamic 3D scenes is a challenging task in 3D vision. Prior
approaches build on NeRF and rely on implicit representations. This is slow since it requires …

We propose a method for editing NeRF scenes with text-instructions. Given a NeRF of a
scene and the collection of images used to reconstruct it, our method uses an image …

The rendering procedure used by neural radiance fields (NeRF) samples a scene with a
single ray per pixel and may therefore produce renderings that are excessively blurred or …

NeRF synthesizes novel views of a scene with unprecedented quality by fitting a neural
radiance field to RGB images. However, NeRF requires querying a deep Multi-Layer …

Emerging neural radiance fields (NeRF) are a promising scene representation for computer
graphics, enabling high-quality 3D reconstruction and novel view synthesis from image …

For the last few decades, several major subfields of artificial intelligence including computer
vision, graphics, and robotics have progressed largely independently from each other …

We present PanopticNeRF, an object-aware neural scene representation that decomposes
a scene into a set of objects (things) and background (stuff). Each object is represented by a …

Rigorously testing autonomy systems is essential for making safe self-driving vehicles (SDV)
a reality. It requires one to generate safety critical scenarios beyond what can be collected …