Loic Landrieu « Structured machine learning for geospatial data
As a PhD candidate at IGN —the French Mapping Agency 🗺—in the machine learning STRUDEL team ( LASTIG lab, IGN - ENSG , Univ. Gustave Eiffel ), and in the CSAI team at ENGIE Lab CRIGEN , I design deep learning methods for multimodal, multi-task learning on large-scale point clouds. Specifically, my recent research involves computer vision on 3D point clouds and 2D images in the wild. You like point clouds ☁ ? You like images 📸 ? You like me 😊.
SPT is a superpoint-based transformer 🤖 architecture that efficiently ⚡ performs semantic segmentation on large-scale 3D scenes. This method includes a fast algorithm that partitions 🧩 point clouds into a hierarchical superpoint structure, as well as a self-attention mechanism to exploit the relationships between superpoints at multiple scales. We reach SOTA on S3DIS 6-Fold (76.0 mIoU), KITTI-360 Val (63.5 mIoU), and DALES (79.6 mIoU)n with: 🦋 212k param. | ⚡ Train S3DIS F5 in 3h | ⌚ SPG preprocessing ÷7
An end-to-end multi-view aggregation method for 3D semantic segmentation from images and point clouds. We reach SOTA on S3DIS and KITTI-360 without requiring point cloud colorization, meshing, or depth sensors: just point clouds ☁, images 📸, and their poses.
Loic Landrieu « Structured machine learning for geospatial data