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20212024
most citedUnsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey

26 citations · 36 across the 8 of their papers we have counts for

collaborators

9 papers

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…

cs.RO2024

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…

cs.RO2024

Multi-Object Navigation in real environments using hybrid policies

Assem Sadek, Guillaume Bono, Boris Chidlovskii +2

Navigation has been classically solved in robotics through the combination of SLAM and planning. More recently, beyond waypoint planning, problems involving significant components…

cs.CV20231 cited

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…