204 citations · 221 across the 8 of their papers we have counts for
10 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…
Faster Maxflow via Improved Dynamic Spectral Vertex Sparsifiers
Jan van den Brand, Yu Gao, Arun Jambulapati +4
We make several advances broadly related to the maintenance of electrical flows in weighted graphs undergoing dynamic resistance updates, including: 1. More efficient dynamic spect…
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…
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…
Multimodal Interaction-aware Motion Prediction for Autonomous Street Crossing
Noha Radwan, Wolfram Burgard, Abhinav Valada
For mobile robots navigating on sidewalks, it is essential to be able to safely cross street intersections. Most existing approaches rely on the recognition of the traffic light si…
VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry
Noha Radwan, Abhinav Valada, Wolfram Burgard
Semantic understanding and localization are fundamental enablers of robot autonomy that have for the most part been tackled as disjoint problems. While deep learning has enabled re…
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…
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…
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…