5 citations · 9 across the 3 of their papers we have counts for
3 papers
Kubric: A scalable dataset generator
Klaus Greff, Francois Belletti, Lucas Beyer +32
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and trainin…
NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes
Suhani Vora, Noha Radwan, Klaus Greff +6
We present NeSF, a method for producing 3D semantic fields from posed RGB images alone. In place of classical 3D representations, our method builds on recent work in implicit neura…
Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations
Mehdi S. M. Sajjadi, Henning Meyer, Etienne Pot +10
A classical problem in computer vision is to infer a 3D scene representation from few images that can be used to render novel views at interactive rates. Previous work focuses on r…