55 citations · 82 across the 8 of their papers we have counts for
10 papers
End-to-end learned Lossy Dynamic Point Cloud Attribute Compression
Dat Thanh Nguyen, Daniel Zieger, Marc Stamminger +1
Recent advancements in point cloud compression have primarily emphasized geometry compression while comparatively fewer efforts have been dedicated to attribute compression. This s…
The Bjøntegaard Bible -- Why your Way of Comparing Video Codecs May Be Wrong
Christian Herglotz, Hannah Och, Anna Meyer +8
In this paper, we provide an in-depth assessment on the Bjøntegaard Delta. We construct a large data set of video compression performance comparisons using a diverse set of metrics…
Deep probabilistic model for lossless scalable point cloud attribute compression
Dat Thanh Nguyen, Kamal Gopikrishnan Nambiar, Andre Kaup
In recent years, several point cloud geometry compression methods that utilize advanced deep learning techniques have been proposed, but there are limited works on attribute compre…
Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model
Dat Thanh Nguyen, Andre Kaup
In recent years, we have witnessed the presence of point cloud data in many aspects of our life, from immersive media, autonomous driving to healthcare, although at the cost of a t…
QC-StyleGAN -- Quality Controllable Image Generation and Manipulation
Dat Viet Thanh Nguyen, Phong Tran The, Tan M. Dinh +2
The introduction of high-quality image generation models, particularly the StyleGAN family, provides a powerful tool to synthesize and manipulate images. However, existing models a…
Learning-based Lossless Point Cloud Geometry Coding using Sparse Tensors
Dat Thanh Nguyen, Andre Kaup
Most point cloud compression methods operate in the voxel or octree domain which is not the original representation of point clouds. Those representations either remove the geometr…
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…
HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections
Ines Chami, Albert Gu, Dat Nguyen +1
This paper studies Principal Component Analysis (PCA) for data lying in hyperbolic spaces. Given directions, PCA relies on: (1) a parameterization of subspaces spanned by these dir…
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…
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…