1 code implementation • 19 Dec 2023 • Monika Wysoczańska, Oriane Siméoni, Michaël Ramamonjisoa, Andrei Bursuc, Tomasz Trzciński, Patrick Pérez
We propose to locally improve dense MaskCLIP features, which are computed with a simple modification of CLIP's last pooling layer, by integrating localization priors extracted from self-supervised features.
1 code implementation • 25 Sep 2023 • Monika Wysoczańska, Michaël Ramamonjisoa, Tomasz Trzciński, Oriane Siméoni
The emergence of CLIP has opened the way for open-world image perception.
1 code implementation • 28 Jul 2022 • Michaël Ramamonjisoa, Sinisa Stekovic, Vincent Lepetit
We present MonteBoxFinder, a method that, given a noisy input point cloud, fits cuboids to the input scene.
1 code implementation • CVPR 2021 • Michaël Ramamonjisoa, Michael Firman, Jamie Watson, Vincent Lepetit, Daniyar Turmukhambetov
We present a novel method for predicting accurate depths from monocular images with high efficiency.
no code implementations • 20 Aug 2019 • Giorgia Pitteri, Michaël Ramamonjisoa, Slobodan Ilic, Vincent Lepetit
Objects with symmetries are common in our daily life and in industrial contexts, but are often ignored in the recent literature on 6D pose estimation from images.
1 code implementation • 21 May 2019 • Michaël Ramamonjisoa, Vincent Lepetit
We demonstrate our approach on the challenging NYUv2-Depth dataset, and show that our method outperforms the state-of-the-art along occluding contours, while performing on par with the best recent methods for the rest of the images.
Ranked #51 on Monocular Depth Estimation on NYU-Depth V2