papers

Publications (10)

cs.CV2022

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…

cs.RO2018

Deep Auxiliary Learning for Visual Localization and Odometry

Abhinav Valada, Noha Radwan, Wolfram Burgard

Localization is an indispensable component of a robot's autonomy stack that enables it to determine where it is in the environment, essentially making it a precursor for any action…

cs.RO2017

Why did the Robot Cross the Road? - Learning from Multi-Modal Sensor Data for Autonomous Road Crossing

Noha Radwan, Wera Winterhalter, Christian Dornhege +1

We consider the problem of developing robots that navigate like pedestrians on sidewalks through city centers for performing various tasks including delivery and surveillance. One…

cs.CV2021

NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections

Ricardo Martin-Brualla, Noha Radwan, Mehdi S. M. Sajjadi +3

We present a learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs. We build on Neural Radiance Fields…

cs.CV2022

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…

cs.CV2017

Topometric Localization with Deep Learning

Gabriel L. Oliveira, Noha Radwan, Wolfram Burgard +1

Compared to LiDAR-based localization methods, which provide high accuracy but rely on expensive sensors, visual localization approaches only require a camera and thus are more cost…