Search Results for author:
Found 7 papers, 3 papers with code
SAFER: Safe Collision Avoidance using Focused and Efficient Trajectory Search with Reinforcement Learning
Collision avoidance is key for mobile robots and agents to operate safely in the real world.
STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset
Specifically, we introduce a synthetic aerial photogrammetry point clouds generation pipeline that takes full advantage of open geospatial data sources and off-the-shelf commercial packages.
Unsupervised Learning of Lidar Features for Use in a Probabilistic Trajectory Estimator
Though the framework is general to any form of parameter learning and sensor modality, we demonstrate application to feature and uncertainty learning with a deep network for 3D lidar odometry.
Variational Inference
Robotics
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor Navigation
We provide insights into our network predictions and show that our approach can also improve the performances of common localization techniques.
Rotation-Invariant Point Convolution With Multiple Equivariant Alignments
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 performances of standard 3D neural networks.
KPConv: Flexible and Deformable Convolution for Point Clouds
Furthermore, these locations are continuous in space and can be learned by the network.
Ranked #1 on
3D Semantic Segmentation
on DALES
Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods
This paper introduces a new definition of multiscale neighborhoods in 3D point clouds.
Ranked #12 on
Semantic Segmentation
on Semantic3D
