16 citations · 24 across the 12 of their papers we have counts for
15 papers
A scalar per patch from pre-trained ViTs enables fast moving navigation in the real world
Steeven Janny, Leonid Antsfeld, Christian Wolf
Trained policies for real-world robotics rely on computer vision components, typically in the form of pre-trained visual encoders. These encoders are an essential component and it…
S-MUSt3R: Sliding Multi-view 3D Reconstruction
Leonid Antsfeld, Boris Chidlovskii, Yohann Cabon +2
The recent paradigm shift in 3D vision led to the rise of foundation models with remarkable capabilities in 3D perception from uncalibrated images. However, extending these models…
PanSt3R: Multi-view Consistent Panoptic Segmentation
Lojze Zust, Yohann Cabon, Juliette Marrie +4
Panoptic segmentation of 3D scenes, involving the segmentation and classification of object instances in a dense 3D reconstruction of a scene, is a challenging problem, especially…
Reasoning in visual navigation of end-to-end trained agents: a dynamical systems approach
Steeven Janny, Hervé Poirier, Leonid Antsfeld +6
Progress in Embodied AI has made it possible for end-to-end-trained agents to navigate in photo-realistic environments with high-level reasoning and zero-shot or language-condition…
MUSt3R: Multi-view Network for Stereo 3D Reconstruction
Yohann Cabon, Lucas Stoffl, Leonid Antsfeld +4
DUSt3R introduced a novel paradigm in geometric computer vision by proposing a model that can provide dense and unconstrained Stereo 3D Reconstruction of arbitrary image collection…
3D-Consistent Image Inpainting with Diffusion Models
Leonid Antsfeld, Boris Chidlovskii
We address the problem of 3D inconsistency of image inpainting based on diffusion models. We propose a generative model using image pairs that belong to the same scene. To achieve…
Self-supervised Pretraining and Finetuning for Monocular Depth and Visual Odometry
Boris Chidlovskii, Leonid Antsfeld
For the task of simultaneous monocular depth and visual odometry estimation, we propose learning self-supervised transformer-based models in two steps. Our first step consists in a…
Zero-BEV: Zero-shot Projection of Any First-Person Modality to BEV Maps
Gianluca Monaci, Leonid Antsfeld, Boris Chidlovskii +1
Bird's-eye view (BEV) maps are an important geometrically structured representation widely used in robotics, in particular self-driving vehicles and terrestrial robots. Existing al…
Learning to navigate efficiently and precisely in real environments
Guillaume Bono, Hervé Poirier, Leonid Antsfeld +3
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…
End-to-End (Instance)-Image Goal Navigation through Correspondence as an Emergent Phenomenon
Guillaume Bono, Leonid Antsfeld, Boris Chidlovskii +2
Most recent work in goal oriented visual navigation resorts to large-scale machine learning in simulated environments. The main challenge lies in learning compact representations g…
Learning with a Mole: Transferable latent spatial representations for navigation without reconstruction
Guillaume Bono, Leonid Antsfeld, Assem Sadek +2
Agents navigating in 3D environments require some form of memory, which should hold a compact and actionable representation of the history of observations useful for decision takin…
CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow
Philippe Weinzaepfel, Thomas Lucas, Vincent Leroy +7
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 mat…
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion
Philippe Weinzaepfel, Vincent Leroy, Thomas Lucas +7
Masked Image Modeling (MIM) has recently been established as a potent pre-training paradigm. A pretext task is constructed by masking patches in an input image, and this masked con…
Magnetic Field Sensing for Pedestrian and Robot Indoor Positioning
Leonid Antsfeld, Boris Chidlovskii
In this paper we address the problem of indoor localization using magnetic field data in two setups, when data is collected by (i) human-held mobile phone and (ii) by localization…
Deep Smartphone Sensors-WiFi Fusion for Indoor Positioning and Tracking
Leonid Antsfeld, Boris Chidlovskii, Emilio Sansano-Sansano
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…