188 citations · 259 across the 6 of their papers we have counts for
5 papers · 1 filter
Exploiting Sparsity in Automotive Radar Object Detection Networks
Marius Lippke, Maurice Quach, Sascha Braun +4
Having precise perception of the environment is crucial for ensuring the secure and reliable functioning of autonomous driving systems. Radar object detection networks are one fund…
Improved Multi-Scale Grid Rendering of Point Clouds for Radar Object Detection Networks
Daniel Köhler, Maurice Quach, Michael Ulrich +3
Architectures that first convert point clouds to a grid representation and then apply convolutional neural networks achieve good performance for radar-based object detection. Howev…
A deep perceptual metric for 3D point clouds
Maurice Quach, Aladine Chetouani, Giuseppe Valenzise +1
Point clouds are essential for storage and transmission of 3D content. As they can entail significant volumes of data, point cloud compression is crucial for practical usage. Recen…
Improved Deep Point Cloud Geometry Compression
Maurice Quach, Giuseppe Valenzise, Frederic Dufaux
Point clouds have been recognized as a crucial data structure for 3D content and are essential in a number of applications such as virtual and mixed reality, autonomous driving, cu…
Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression
Maurice Quach, Giuseppe Valenzise, Frederic Dufaux
Efficient point cloud compression is fundamental to enable the deployment of virtual and mixed reality applications, since the number of points to code can range in the order of mi…