17 citations · 47 across the 7 of their papers we have counts for
6 papers · 1 filter
BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point Clouds
Corentin Sautier, Gilles Puy, Alexandre Boulch +2
We present a surprisingly simple and efficient method for self-supervision of 3D backbone on automotive Lidar point clouds. We design a contrastive loss between features of Lidar s…
DiffHPE: Robust, Coherent 3D Human Pose Lifting with Diffusion
Cédric Rommel, Eduardo Valle, Mickaël Chen +4
We present an innovative approach to 3D Human Pose Estimation (3D-HPE) by integrating cutting-edge diffusion models, which have revolutionized diverse fields, but are relatively un…
POCO: Point Convolution for Surface Reconstruction
Alexandre Boulch, Renaud Marlet
Implicit neural networks have been successfully used for surface reconstruction from point clouds. However, many of them face scalability issues as they encode the isosurface funct…
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…
Crafting a multi-task CNN for viewpoint estimation
Francisco Massa, Renaud Marlet, Mathieu Aubry
Convolutional Neural Networks (CNNs) were recently shown to provide state-of-the-art results for object category viewpoint estimation. However different ways of formulating this pr…
Convolutional Neural Networks for joint object detection and pose estimation: A comparative study
Francisco Massa, Mathieu Aubry, Renaud Marlet
In this paper we study the application of convolutional neural networks for jointly detecting objects depicted in still images and estimating their 3D pose. We identify different f…