188 citations · 259 across the 6 of their papers we have counts for
10 papers
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…
BASICS: Broad quality Assessment of Static point clouds In Compression Scenarios
Ali Ak, Emin Zerman, Maurice Quach +4
Point clouds have become increasingly prevalent in representing 3D scenes within virtual environments, alongside 3D meshes. Their ease of capture has facilitated a wide array of ap…
Lossless Coding of Point Cloud Geometry using a Deep Generative Model
Dat Thanh Nguyen, Maurice Quach, Giuseppe Valenzise +1
This paper proposes a lossless point cloud (PC) geometry compression method that uses neural networks to estimate the probability distribution of voxel occupancy. First, to take in…
Multiscale deep context modeling for lossless point cloud geometry compression
Dat Thanh Nguyen, Maurice Quach, Giuseppe Valenzise +1
We propose a practical deep generative approach for lossless point cloud geometry compression, called MSVoxelDNN, and show that it significantly reduces the rate compared to the MP…
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…
Learning-based lossless compression of 3D point cloud geometry
Dat Thanh Nguyen, Maurice Quach, Giuseppe Valenzise +1
This paper presents a learning-based, lossless compression method for static point cloud geometry, based on context-adaptive arithmetic coding. Unlike most existing methods working…
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…
Folding-based compression of point cloud attributes
Maurice Quach, Giuseppe Valenzise, Frederic Dufaux
Existing techniques to compress point cloud attributes leverage either geometric or video-based compression tools. We explore a radically different approach inspired by recent adva…
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…