2 citations · 3 across the 2 of their papers we have counts for
5 papers
Unsupervised Learning of Lidar Features for Use in a Probabilistic Trajectory Estimator
David J. Yoon, Haowei Zhang, Mona Gridseth +2
We present unsupervised parameter learning in a Gaussian variational inference setting that combines classic trajectory estimation for mobile robots with deep learning for rich sen…
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor Navigation
Hugues Thomas, Ben Agro, Mona Gridseth +2
We present a self-supervised learning approach for the semantic segmentation of lidar frames. Our method is used to train a deep point cloud segmentation architecture without any h…
Rotation-Invariant Point Convolution With Multiple Equivariant Alignments
Hugues Thomas
Recent attempts at introducing rotation invariance or equivariance in 3D deep learning approaches have shown promising results, but these methods still struggle to reach the perfor…
KPConv: Flexible and Deformable Convolution for Point Clouds
Hugues Thomas, Charles R. Qi, Jean-Emmanuel Deschaud +3
We present Kernel Point Convolution (KPConv), a new design of point convolution, i.e. that operates on point clouds without any intermediate representation. The convolution weights…
Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods
Hugues Thomas, Jean-Emmanuel Deschaud, Beatriz Marcotegui +2
This paper introduces a new definition of multiscale neighborhoods in 3D point clouds. This definition, based on spherical neighborhoods and proportional subsampling, allows the co…