12 citations · 27 across the 16 of their papers we have counts for
16 papers
A Model You Can Hear: Audio Identification with Playable Prototypes
Romain Loiseau, Baptiste Bouvier, Yann Teytaut +3
Machine learning techniques have proved useful for classifying and analyzing audio content. However, recent methods typically rely on abstract and high-dimensional representations…
Online Segmentation of LiDAR Sequences: Dataset and Algorithm
Romain Loiseau, Mathieu Aubry, Loïc Landrieu
Roof-mounted spinning LiDAR sensors are widely used by autonomous vehicles. However, most semantic datasets and algorithms used for LiDAR sequence segmentation operate on $360^\cir…
Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans
Ekaterina Kalinicheva, Loic Landrieu, Clément Mallet +1
The analysis of the multi-layer structure of wild forests is an important challenge of automated large-scale forestry. While modern aerial LiDARs offer geometric information across…
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…
Predicting Vegetation Stratum Occupancy from Airborne LiDAR Data with Deep Learning
Ekaterina Kalinicheva, Loic Landrieu, Clément Mallet +1
We propose a new deep learning-based method for estimating the occupancy of vegetation strata from airborne 3D LiDAR point clouds. Our model predicts rasterized occupancy maps for…
Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds
Ekaterina Kalinicheva, Loic Landrieu, Clément Mallet +1
We propose a new deep learning-based method for estimating the occupancy of vegetation strata from 3D point clouds captured from an aerial platform. Our model predicts rasterized o…