1 code implementation • 8 Nov 2022 • Henrique Weber, Mathieu Garon, Jean-François Lalonde
We present a method for estimating lighting from a single perspective image of an indoor scene.
1 code implementation • 9 Jun 2020 • Etienne Dubeau, Mathieu Garon, Benoit Debaque, Raoul de Charette, Jean-François Lalonde
In this paper, we propose, for the first time, to use an event-based camera to increase the speed of 3D object tracking in 6 degrees of freedom.
1 code implementation • 7 Feb 2020 • Sébastien de Blois, Mathieu Garon, Christian Gagné, Jean-François Lalonde
Computer vision datasets containing multiple modalities such as color, depth, and thermal properties are now commonly accessible and useful for solving a wide array of challenging tasks.
1 code implementation • 26 Nov 2019 • Jean-Philippe Mercier, Mathieu Garon, Philippe Giguère, Jean-François Lalonde
In this context, we propose a generic 2D object instance detection approach that uses example viewpoints of the target object at test time to retrieve its 2D location in RGB images, without requiring any additional training (i. e. fine-tuning) step.
no code implementations • CVPR 2019 • Mathieu Garon, Kalyan Sunkavalli, Sunil Hadap, Nathan Carr, Jean-François Lalonde
We propose a real-time method to estimate spatiallyvarying indoor lighting from a single RGB image.
1 code implementation • ECCV 2018 • Mathieu Garon, Denis Laurendeau, Jean-François Lalonde
We present a challenging and realistic novel dataset for evaluating 6-DOF object tracking algorithms.
no code implementations • 28 Mar 2017 • Mathieu Garon, Jean-François Lalonde
We present a temporal 6-DOF tracking method which leverages deep learning to achieve state-of-the-art performance on challenging datasets of real world capture.