Search Results for author:
Found 24 papers, 4 papers with code
Zero-BEV: Zero-shot Projection of Any First-Person Modality to BEV Maps
Bird's-eye view (BEV) maps are an important geometrically structured representation widely used in robotics, in particular self-driving vehicles and terrestrial robots.
Learning to navigate efficiently and precisely in real environments
In the context of autonomous navigation of terrestrial robots, the creation of realistic models for agent dynamics and sensing is a widespread habit in the robotics literature and in commercial applications, where they are used for model based control and/or for localization and mapping.
Multi-Object Navigation in real environments using hybrid policies
More recently, beyond waypoint planning, problems involving significant components of (visual) high-level reasoning have been explored in simulated environments, mostly addressed with large-scale machine learning, in particular RL, offline-RL or imitation learning.
DUSt3R: Geometric 3D Vision Made Easy
Our formulation directly provides a 3D model of the scene as well as depth information, but interestingly, we can seamlessly recover from it, pixel matches, relative and absolute camera.
End-to-End (Instance)-Image Goal Navigation through Correspondence as an Emergent Phenomenon
The main challenge lies in learning compact representations generalizable to unseen environments and in learning high-capacity perception modules capable of reasoning on high-dimensional input.
Semantic Image Segmentation: Two Decades of Research
Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer vision applications, providing key information for the global understanding of an image.
CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow
Despite impressive performance for high-level downstream tasks, self-supervised pre-training methods have not yet fully delivered on dense geometric vision tasks such as stereo matching or optical flow.
Ranked #1 on
Optical Flow Estimation
on KITTI 2012
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion
More precisely, we propose the pretext task of cross-view completion where the first input image is partially masked, and this masked content has to be reconstructed from the visible content and the second image.
PUMP: Pyramidal and Uniqueness Matching Priors for Unsupervised Learning of Local Descriptors
In this paper, we propose to explicitly integrate two matching priors in a single loss in order to learn local descriptors without supervision.
Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey
Semantic segmentation plays a fundamental role in a broad variety of computer vision applications, providing key information for the global understanding of an image.
An in-depth experimental study of sensor usage and visual reasoning of robots navigating in real environments
In this work we present an in-depth study of the performance and reasoning capacities of real physical agents, trained in simulation and deployed to two different physical environments.
Magnetic Field Sensing for Pedestrian and Robot Indoor Positioning
For the first setup, we revise the state of the art approaches and propose a novel extended pipeline to benefit from the presence of magnetic anomalies in indoor environment created by different ferromagnetic objects.
Learning From Long-Tailed Data With Noisy Labels
In addition, the resulting learned representations are also remarkably robust to label noise, when fine-tuned with an imbalance- and noise-resistant loss function.
Universal Domain Adaptation in Ordinal Regression
We address the problem of universal domain adaptation (UDA) in ordinal regression (OR), which attempts to solve classification problems in which labels are not independent, but follow a natural order.
Deep Smartphone Sensors-WiFi Fusion for Indoor Positioning and Tracking
We address the indoor localization problem, where the goal is to predict user's trajectory from the data collected by their smartphone, using inertial sensors such as accelerometer, gyroscope and magnetometer, as well as other environment and network sensors such as barometer and WiFi.
Learning Synthetic to Real Transfer for Localization and Navigational Tasks
The navigation pipeline is decomposed as a localization module, a planning module and a local navigation module.
Adversarial Transfer of Pose Estimation Regression
We address the problem of camera pose estimation in visual localization.
Self-Supervised Attention Learning for Depth and Ego-motion Estimation
We address the problem of depth and ego-motion estimation from image sequences.
Using Latent Codes for Class Imbalance Problem in Unsupervised Domain Adaptation
We address the problem of severe class imbalance in unsupervised domain adaptation, when the class spaces in source and target domains diverge considerably.
Learning Common Representation from RGB and Depth Images
We propose a new deep learning architecture for the tasks of semantic segmentation and depth prediction from RGB-D images.
Multi-task learning of time series and its application to the travel demand
We address the problem of modeling and prediction of a set of temporal events in the context of intelligent transportation systems.
Mining Smart Card Data for Travelers' Mini Activities
In the context of public transport modeling and simulation, we address the problem of mismatch between simulated transit trips and observed ones.
