5 papers
Sparse auto-regressive modeling for scene generation from multi-view images
Thomas Lucas, Maxime Pietrantoni, Philippe Weinzaepfel +4
Generating complete 3D scenes from sparse, unconstrained views is a fundamental challenge in 3D vision which requires reasoning beyond observed content while remaining computationa…
Can we make NeRF-based visual localization privacy-preserving?
Maxime Pietrantoni, Martin Humenberger, Torsten Sattler +1
Visual localization (VL) is the task of estimating the camera pose in a known scene. VL methods, a.o., can be distinguished based on how they represent the scene, e.g., explicitly…
Gaussian Splatting Feature Fields for Privacy-Preserving Visual Localization
Maxime Pietrantoni, Gabriela Csurka, Torsten Sattler
Visual localization is the task of estimating a camera pose in a known environment. In this paper, we utilize 3D Gaussian Splatting (3DGS)-based representations for accurate and pr…
Self-supervised Learning of Neural Implicit Feature Fields for Camera Pose Refinement
Maxime Pietrantoni, Gabriela Csurka, Martin Humenberger +1
Visual localization techniques rely upon some underlying scene representation to localize against. These representations can be explicit such as 3D SFM map or implicit, such as a n…
Learning Synthetic to Real Transfer for Localization and Navigational Tasks
Maxime Pietrantoni, Boris Chidlovskii, Tomi Silander
Autonomous navigation consists in an agent being able to navigate without human intervention or supervision, it affects both high level planning and low level control. Navigation i…