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20162026
most citedLocalizing Objects with Self-Supervised Transformers and no Labels

107 citations · 215 across the 36 of their papers we have counts for

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Showing 2021 · cs.CVShow all

6 papers · 2 filters

cs.CV2021★ 14 cited

NeeDrop: Self-supervised Shape Representation from Sparse Point Clouds using Needle Dropping

Alexandre Boulch, Pierre-Alain Langlois, Gilles Puy +1

There has been recently a growing interest for implicit shape representations. Contrary to explicit representations, they have no resolution limitations and they easily deal with a…

cs.CV2021

PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds

Anh-Quan Cao, Gilles Puy, Alexandre Boulch +1

Rigid registration of point clouds with partial overlaps is a longstanding problem usually solved in two steps: (a) finding correspondences between the point clouds; (b) filtering…

cs.CV2021★ 107 cited

Localizing Objects with Self-Supervised Transformers and no Labels

Oriane Siméoni, Gilles Puy, Huy V. Vo +6

Localizing objects in image collections without supervision can help to avoid expensive annotation campaigns. We propose a simple approach to this problem, that leverages the activ…

cs.CV2021

Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds

Björn Michele, Alexandre Boulch, Gilles Puy +2

While there has been a number of studies on Zero-Shot Learning (ZSL) for 2D images, its application to 3D data is still recent and scarce, with just a few methods limited to classi…

cs.CV2021

Scalable Surface Reconstruction with Delaunay-Graph Neural Networks

Raphael Sulzer, Loic Landrieu, Renaud Marlet +1

We introduce a novel learning-based, visibility-aware, surface reconstruction method for large-scale, defect-laden point clouds. Our approach can cope with the scale and variety of…

cs.CV2021

PoseContrast: Class-Agnostic Object Viewpoint Estimation in the Wild with Pose-Aware Contrastive Learning

Yang Xiao, Yuming Du, Renaud Marlet

Motivated by the need for estimating the 3D pose of arbitrary objects, we consider the challenging problem of class-agnostic object viewpoint estimation from images only, without C…