activity
20182021
most citedDeep Implicit Volume Compression

5 citations · 5 across the 2 of their papers we have counts for

collaborators

9 papers

cs.CV2021

3D Scene Compression through Entropy Penalized Neural Representation Functions

Thomas Bird, Johannes Ballé, Saurabh Singh +1

Some forms of novel visual media enable the viewer to explore a 3D scene from arbitrary viewpoints, by interpolating between a discrete set of original views. Compared to 2D imager…

eess.SP2020

Spectral folding and two-channel filter-banks on arbitrary graphs

Eduardo Pavez, Benjamin Girault, Antonio Ortega +1

In the past decade, several multi-resolution representation theories for graph signals have been proposed. Bipartite filter-banks stand out as the most natural extension of time do…

cs.IT2020

Nonlinear Transform Coding

Johannes Ballé, Philip A. Chou, David Minnen +5

We review a class of methods that can be collected under the name nonlinear transform coding (NTC), which over the past few years have become competitive with the best linear trans…

eess.IV20205 cited

Deep Implicit Volume Compression

Danhang Tang, Saurabh Singh, Philip A. Chou +11

We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in 3D voxel grids, and their corresponding textures. To compress the TSDF, our method re…

cs.CV2020

Region adaptive graph fourier transform for 3d point clouds

Eduardo Pavez, Benjamin Girault, Antonio Ortega +1

We introduce the Region Adaptive Graph Fourier Transform (RA-GFT) for compression of 3D point cloud attributes. The RA-GFT is a multiresolution transform, formed by combining spati…

eess.IV2018

A Volumetric Approach to Point Cloud Compression

Maja Krivokuća, Maxim Koroteev, Philip A. Chou

Compression of point clouds has so far been confined to coding the positions of a discrete set of points in space and the attributes of those discrete points. We introduce an alter…