Author name cluster

Noha Radwan

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4 papers

2 author rows

TMLR Journal 2022 Journal Article

NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes

Suhani Vora
Noha Radwan
Klaus Greff
Henning Meyer
Kyle Genova
Mehdi S. M. Sajjadi
Etienne Pot
Andrea Tagliasacchi

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 neural fields wherein 3D structure is captured by point-wise functions. We leverage this methodology to recover 3D density fields upon which we then train a 3D semantic segmentation model supervised by posed 2D semantic maps. Despite being trained on 2D signals alone, our method is able to generate 3D-consistent semantic maps from novel camera poses and can be queried at arbitrary 3D points. Notably, NeSF is compatible with any method producing a density field. Our empirical analysis demonstrates comparable quality to competitive 2D and 3D semantic segmentation baselines on complex, realistically-rendered scenes and significantly outperforms a comparable neural radiance field-based method on a series of tasks requiring 3D reasoning. Our method is the first to learn semantics by recognizing patterns in the geometry stored within a 3D neural field representation. NeSF is trained using purely 2D signals and requires as few as one labeled image per-scene at train time. No semantic input is required for inference on novel scenes.

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ICRA Conference 2018 Conference Paper

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 execution or planning. Although convolutional neural networks have shown promising results for visual localization, they are still grossly outperformed by state-of-the-art local feature-based techniques. In this work, we propose VLocNet, a new convolutional neural network architecture for 6-DoF global pose regression and odometry estimation from consecutive monocular images. Our multitask model incorporates hard parameter sharing, thus being compact and enabling real-time inference, in addition to being end-to-end trainable. We propose a novel loss function that utilizes auxiliary learning to leverage relative pose information during training, thereby constraining the search space to obtain consistent pose estimates. We evaluate our proposed VLocNet on indoor as well as outdoor datasets and show that even our single task model exceeds the performance of state-of-the-art deep architectures for global localization, while achieving competitive performance for visual odometry estimation. Furthermore, we present extensive experimental evaluations utilizing our proposed Geometric Consistency Loss that show the effectiveness of multitask learning and demonstrate that our model is the first deep learning technique to be on par with, and in some cases outperforms state-of-the-art SIFT-based approaches.

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IROS Conference 2017 Conference Paper

Why did the robot cross the road? - Learning from multi-modal sensor data for autonomous road crossing

Noha Radwan
Wera Winterhalter
Christian Dornhege
Wolfram Burgard

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 particular challenge for such robots is crossing streets without pedestrian traffic lights. To solve this task the robot has to decide based on its sensory input if the road is clear. In this work, we propose a novel multi-modal learning approach for the problem of autonomous street crossing. Our approach solely relies on laser and radar data and learns a classifier based on Random Forests to predict when it is safe to cross the road. We present extensive experimental evaluations using real-world data collected from multiple street crossing situations which demonstrate that our approach yields a safe and accurate street crossing behavior and generalizes well over different types of situations. A comparison to alternative methods demonstrates the advantages of our approach.

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ICRA Conference 2016 Conference Paper

Do you see the bakery? Leveraging geo-referenced texts for global localization in public maps

Noha Radwan
Gian Diego Tipaldi
Luciano Spinello
Wolfram Burgard

Text is one of the richest sources of information in an urban environment. Although textual information is heavily relied on by humans for a majority of the daily tasks, its usage has not been completely exploited in the field of robotics. In this work, we propose a localization approach utilizing textual features in urban environments. Starting at an unknown location, equipped with an RGB-camera and a compass, our approach uses off-the-shelf text extraction methods to identify text labels in the vicinity. We then apply a probabilistic localization approach with specific sensor models to integrate multiple observations. An extensive evaluation with real-world data gathered in different cities reveals an improvement over GPS-based localization when using our method.

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