107 citations · 288 across the 43 of their papers we have counts for
6 papers · 1 filter
ALSO: Automotive Lidar Self-supervision by Occupancy estimation
Alexandre Boulch, Corentin Sautier, Björn Michele +2
We propose a new self-supervised method for pre-training the backbone of deep perception models operating on point clouds. The core idea is to train the model on a pretext task whi…
A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation
Georgy Ponimatkin, Nermin Samet, Yang Xiao +3
We propose a simple, yet powerful approach for unsupervised object segmentation in videos. We introduce an objective function whose minimum represents the mask of the main salient…
Take One Gram of Neural Features, Get Enhanced Group Robustness
Simon Roburin, Charles Corbière, Gilles Puy +4
Predictive performance of machine learning models trained with empirical risk minimization (ERM) can degrade considerably under distribution shifts. The presence of spurious correl…
Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data
Corentin Sautier, Gilles Puy, Spyros Gidaris +3
Segmenting or detecting objects in sparse Lidar point clouds are two important tasks in autonomous driving to allow a vehicle to act safely in its 3D environment. The best performi…
Deep Surface Reconstruction from Point Clouds with Visibility Information
Raphael Sulzer, Loic Landrieu, Alexandre Boulch +2
Most current neural networks for reconstructing surfaces from point clouds ignore sensor poses and only operate on raw point locations. Sensor visibility, however, holds meaningful…
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…