Representing geometry data as voxels allows for a massive amount of detail that can be rendered in real-time. Storing this type of data as a directed acyclic graph (DAG) has recently led to immense improvements in memory consumption, which is one of the main limitations often associated with voxel-based approaches. We present a method for further decreasing the memory consumption of voxel data in a DAG through a form of lossy compression, meaning that the data is slightly altered from its original state. Our method clusters similar nodes in the graph together in a way that minimizes the geometric error that is introduced. The amount of compression that is applied can be influenced through a set of parameters that affect which nodes are incl...
International audienceThis paper presents a learning-based, lossless compression method for static p...
International audienceThis paper proposes a lossless point cloud (PC) geometry compression method th...
International audienceWe propose a practical deep generative approach for lossless point cloud geome...
Sparse Voxel Directed Acyclic Graphs (SVDAGs) losslessly compress highly detailed geometry in a high...
Voxel-based approaches are today’s standard to encode volume data. Recently, directed acyclic graphs...
Voxels are a popular choice to encode complex geometry. Their regularity makes updates easy and enab...
Sparse Voxel Directed Acyclic Graphs (SVDAGs) are an efficient solution for storing high-resolution ...
We show that a binary voxel grid can be represented orders of magnitude more efficiently than using ...
This thesis investigates a memory-efficient representation of highly detailed geometry in 3D voxel g...
We explore the problem of decoupling color information from geometry in large scenes of voxelized su...
International audienceWe present two learning-based methods for coding point clouds geometry. The tw...
In computer graphics, the geometry of virtual worlds can be represented in numerousways, from collec...
This paper deals with the issue of geometry representation of voxelized three-dimensional scenes usi...
This paper presents a technique for lossy compression of dense range images. Two separate compressio...
In this article we propose a method of mesh compression and streaming, that can be used for real-tim...
International audienceThis paper presents a learning-based, lossless compression method for static p...
International audienceThis paper proposes a lossless point cloud (PC) geometry compression method th...
International audienceWe propose a practical deep generative approach for lossless point cloud geome...
Sparse Voxel Directed Acyclic Graphs (SVDAGs) losslessly compress highly detailed geometry in a high...
Voxel-based approaches are today’s standard to encode volume data. Recently, directed acyclic graphs...
Voxels are a popular choice to encode complex geometry. Their regularity makes updates easy and enab...
Sparse Voxel Directed Acyclic Graphs (SVDAGs) are an efficient solution for storing high-resolution ...
We show that a binary voxel grid can be represented orders of magnitude more efficiently than using ...
This thesis investigates a memory-efficient representation of highly detailed geometry in 3D voxel g...
We explore the problem of decoupling color information from geometry in large scenes of voxelized su...
International audienceWe present two learning-based methods for coding point clouds geometry. The tw...
In computer graphics, the geometry of virtual worlds can be represented in numerousways, from collec...
This paper deals with the issue of geometry representation of voxelized three-dimensional scenes usi...
This paper presents a technique for lossy compression of dense range images. Two separate compressio...
In this article we propose a method of mesh compression and streaming, that can be used for real-tim...
International audienceThis paper presents a learning-based, lossless compression method for static p...
International audienceThis paper proposes a lossless point cloud (PC) geometry compression method th...
International audienceWe propose a practical deep generative approach for lossless point cloud geome...