1 citations · 2 across the 12 of their papers we have counts for
16 papers
StyleFields: Multi-Scale AdaIN-Modulated Implicit SDFs for Coarse-to-Fine 3D Shape Reconstruction and Editing
Ehsan Garaaghaji, Nicolas Talabot, Pascal Fua +1
We introduce StyleFields, a DeepSDF-based architecture for high-fidelity 3D reconstruction that enables controllable geometric style mixing: the coarse structure of one object can…
Revisiting Multi-View Stereo: A Sequence-to-Sequence Formulation
Aoxiang Fan, Corentin Dumery, Nicolas Talabot +1
Computing accurate geometry from multi-view images is a fundamental problem in computer vision. Recent feed-forward (FF) models jointly estimate 3D geometry and camera parameters,…
Automated Counting of Stacked Objects in Industrial Inspection
Corentin Dumery, Noa Etté, Aoxiang Fan +4
Visual object counting is a fundamental computer vision task in industrial inspection, where accurate, high-throughput inventory tracking and quality assurance are critical. Moreov…
High Resolution UDF Meshing via Iterative Networks
Federico Stella, Nicolas Talabot, Hieu Le +1
Unsigned Distance Fields (UDFs) are a natural implicit representation for open surfaces but, unlike Signed Distance Fields (SDFs), are challenging to triangulate into explicit mesh…
Generalizable Neural Reconstruction of High-Fidelity Surfaces via Sparse Volumetric Representations
Aoxiang Fan, Corentin Dumery, Nicolas Talabot +3
Neural implicit representations have recently achieved impressive results in novel view synthesis and multi-view 3D reconstruction, yet both NeRF- and Gaussian Splatting-based meth…
A View-consistent Sampling Method for Regularized Training of Neural Radiance Fields
Aoxiang Fan, Corentin Dumery, Nicolas Talabot +1
Neural Radiance Fields (NeRF) has emerged as a compelling framework for scene representation and 3D recovery. To improve its performance on real-world data, depth regularizations h…
Diffusion Models are Secretly Zero-Shot 3DGS Harmonizers
Vsevolod Skorokhodov, Nikita Durasov, Pascal Fua
Gaussian Splatting has become a popular technique for various 3D Computer Vision tasks, including novel view synthesis, scene reconstruction, and dynamic scene rendering. However,…
PartSDF: Part-Based Implicit Neural Representation for Composite 3D Shape Parametrization and Optimization
Nicolas Talabot, Olivier Clerc, Arda Cinar Demirtas +4
Accurate 3D shape representation is essential in engineering applications such as design, optimization, and simulation. In practice, engineering workflows require structured, part-…
Counting Stacked Objects
Corentin Dumery, Noa Etté, Aoxiang Fan +4
Visual object counting is a fundamental computer vision task underpinning numerous real-world applications, from cell counting in biomedicine to traffic and wildlife monitoring. Ho…
Uncertainty Estimation for 3D Object Detection via Evidential Learning
Nikita Durasov, Rafid Mahmood, Jiwoong Choi +4
3D object detection is an essential task for computer vision applications in autonomous vehicles and robotics. However, models often struggle to quantify detection reliability, lea…
Gradient Distance Function
Hieu Le, Federico Stella, Benoit Guillard +1
Unsigned Distance Functions (UDFs) can be used to represent non-watertight surfaces in a deep learning framework. However, UDFs tend to be brittle and difficult to learn, in part b…
IT: Idempotent Test-Time Training
Nikita Durasov, Assaf Shocher, Doruk Oner +3
Deep learning models often struggle when deployed in real-world settings due to distribution shifts between training and test data. While existing approaches like domain adaptation…
Vision-Based Power Line Cables and Pylons Detection for Low Flying Aircraft
Jakub Gwizdała, Doruk Oner, Soumava Kumar Roy +6
Power lines are dangerous for low-flying aircraft, especially in low-visibility conditions. Thus, a vision-based system able to analyze the aircraft's surroundings and to provide t…
Neural Surface Detection for Unsigned Distance Fields
Federico Stella, Nicolas Talabot, Hieu Le +1
Extracting surfaces from Signed Distance Fields (SDFs) can be accomplished using traditional algorithms, such as Marching Cubes. However, since they rely on sign flips across the s…
MirrorCheck: Efficient Adversarial Defense for Vision-Language Models
Samar Fares, Klea Ziu, Toluwani Aremu +5
Vision-Language Models (VLMs) are increasingly susceptible to sophisticated adversarial attacks, including adaptive strategies specifically designed to bypass existing defenses. To…
Enabling Uncertainty Estimation in Iterative Neural Networks
Nikita Durasov, Doruk Oner, Jonathan Donier +2
Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that…