Search Results for author: Loic Landrieu

Found 22 papers, 19 papers with code

Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs

2 code implementations CVPR 2018 Loic Landrieu, Martin Simonovsky

We propose a novel deep learning-based framework to tackle the challenge of semantic segmentation of large-scale point clouds of millions of points.

3D Semantic Segmentation

Supervized Segmentation with Graph-Structured Deep Metric Learning

1 code implementation10 May 2019 Loic Landrieu, Mohamed Boussaha

We introduce the graph-structured contrastive loss, a loss function structured by a ground truth segmentation.

Metric Learning Segmentation

Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention

2 code implementations CVPR 2020 Vivien Sainte Fare Garnot, Loic Landrieu, Sebastien Giordano, Nesrine Chehata

Satellite image time series, bolstered by their growing availability, are at the forefront of an extensive effort towards automated Earth monitoring by international institutions.

General Classification Time Series +2

Lightweight Temporal Self-Attention for Classifying Satellite Image Time Series

1 code implementation1 Jul 2020 Vivien Sainte Fare Garnot, Loic Landrieu

The increasing accessibility and precision of Earth observation satellite data offers considerable opportunities for industrial and state actors alike.

Earth Observation Time Series +2

Leveraging Class Hierarchies with Metric-Guided Prototype Learning

1 code implementation6 Jul 2020 Vivien Sainte Fare Garnot, Loic Landrieu

In this paper, we propose to model the hierarchical class structure by integrating this metric in the supervision of a prototypical network.

General Classification Semantic Segmentation +3

Torch-Points3D: A Modular Multi-Task Frameworkfor Reproducible Deep Learning on 3D Point Clouds

6 code implementations9 Oct 2020 Thomas Chaton, Nicolas Chaulet, Sofiane Horache, Loic Landrieu

We introduce Torch-Points3D, an open-source framework designed to facilitate the use of deep networks on3D data.

Scalable Surface Reconstruction with Delaunay-Graph Neural Networks

1 code implementation13 Jul 2021 Raphael Sulzer, Loic Landrieu, Renaud Marlet, Bruno Vallet

We introduce a novel learning-based, visibility-aware, surface reconstruction method for large-scale, defect-laden point clouds.

Surface Reconstruction

Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series

1 code implementation15 Oct 2021 Félix Quinton, Loic Landrieu

While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping.

Classification Crop Classification +3

Multi-Modal Temporal Attention Models for Crop Mapping from Satellite Time Series

1 code implementation14 Dec 2021 Vivien Sainte Fare Garnot, Loic Landrieu, Nesrine Chehata

Motivated by the recent success of temporal attention-based methods across multiple crop mapping tasks, we propose to investigate how these models can be adapted to operate on several modalities.

Panoptic Segmentation Time Series +1

Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds

no code implementations27 Dec 2021 Ekaterina Kalinicheva, Loic Landrieu, Clément Mallet, Nesrine Chehata

We propose a new deep learning-based method for estimating the occupancy of vegetation strata from 3D point clouds captured from an aerial platform.

regression

Predicting Vegetation Stratum Occupancy from Airborne LiDAR Data with Deep Learning

no code implementations20 Jan 2022 Ekaterina Kalinicheva, Loic Landrieu, Clément Mallet, Nesrine Chehata

We propose a new deep learning-based method for estimating the occupancy of vegetation strata from airborne 3D LiDAR point clouds.

regression

Deep Surface Reconstruction from Point Clouds with Visibility Information

1 code implementation3 Feb 2022 Raphael Sulzer, Loic Landrieu, Alexandre Boulch, Renaud Marlet, Bruno Vallet

Most current neural networks for reconstructing surfaces from point clouds ignore sensor poses and only operate on raw point locations.

Surface Reconstruction

Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation

1 code implementation CVPR 2022 Damien Robert, Bruno Vallet, Loic Landrieu

Recent works on 3D semantic segmentation propose to exploit the synergy between images and point clouds by processing each modality with a dedicated network and projecting learned 2D features onto 3D points.

 Ranked #1 on 3D Semantic Segmentation on KITTI-360 (using extra training data)

3D Semantic Segmentation Colorization +1

Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans

1 code implementation25 Apr 2022 Ekaterina Kalinicheva, Loic Landrieu, Clément Mallet, Nesrine Chehata

The analysis of the multi-layer structure of wild forests is an important challenge of automated large-scale forestry.

A Survey and Benchmark of Automatic Surface Reconstruction from Point Clouds

1 code implementation31 Jan 2023 Raphael Sulzer, Renaud Marlet, Bruno Vallet, Loic Landrieu

We present a comprehensive survey and benchmark of both traditional and learning-based methods for surface reconstruction from point clouds.

Surface Reconstruction

Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans

1 code implementation19 Apr 2023 Romain Loiseau, Elliot Vincent, Mathieu Aubry, Loic Landrieu

We demonstrate the usefulness of our model on a novel dataset of seven large aerial LiDAR scans from diverse real-world scenarios.

Efficient 3D Semantic Segmentation with Superpoint Transformer

1 code implementation ICCV 2023 Damien Robert, Hugo Raguet, Loic Landrieu

We introduce a novel superpoint-based transformer architecture for efficient semantic segmentation of large-scale 3D scenes.

 Ranked #1 on 3D Semantic Segmentation on S3DIS (mIoU (6-Fold) metric)

3D Semantic Segmentation

FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery

1 code implementation20 Oct 2023 Anatol Garioud, Nicolas Gonthier, Loic Landrieu, Apolline De Wit, Marion Valette, Marc Poupée, Sébastien Giordano, Boris Wattrelos

We introduce the French Land cover from Aerospace ImageRy (FLAIR), an extensive dataset from the French National Institute of Geographical and Forest Information (IGN) that provides a unique and rich resource for large-scale geospatial analysis.

Land Cover Classification Semantic Segmentation +1

Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering

1 code implementation12 Jan 2024 Damien Robert, Hugo Raguet, Loic Landrieu

We introduce a highly efficient method for panoptic segmentation of large 3D point clouds by redefining this task as a scalable graph clustering problem.

3D Semantic Segmentation Graph Clustering +2

Cannot find the paper you are looking for? You can Submit a new open access paper.