Search Results for author:
Found 14 papers, 3 papers with code
One-Click Upgrade from 2D to 3D: Sandwiched RGB-D Video Compression for Stereoscopic Teleconferencing
In this paper, we propose a new approach to upgrade a 2D video codec to support stereo RGB-D video compression, by wrapping it with a neural pre- and post-processor pair.
Sandwiched Compression: Repurposing Standard Codecs with Neural Network Wrappers
We propose sandwiching standard image and video codecs between pre- and post-processing neural networks.
Learned Nonlinear Predictor for Critically Sampled 3D Point Cloud Attribute Compression
We study 3D point cloud attribute compression via a volumetric approach: assuming point cloud geometry is known at both encoder and decoder, parameters $\theta$ of a continuous attribute function $f: \mathbb{R}^3 \mapsto \mathbb{R}$ are quantized to $\hat{\theta}$ and encoded, so that discrete samples $f_{\hat{\theta}}(\mathbf{x}_i)$ can be recovered at known 3D points $\mathbf{x}_i \in \mathbb{R}^3$ at the decoder.
Volumetric 3D Point Cloud Attribute Compression: Learned polynomial bilateral filter for prediction
We extend a previous study on 3D point cloud attribute compression scheme that uses a volumetric approach: given a target volumetric attribute function $f : \mathbb{R}^3 \mapsto \mathbb{R}$, we quantize and encode parameters $\theta$ that characterize $f$ at the encoder, for reconstruction $f_{\hat{\theta}}(\mathbf(x))$ at known 3D points $\mathbf(x)$ at the decoder.
Volumetric Attribute Compression for 3D Point Clouds using Feedforward Network with Geometric Attention
We study 3D point cloud attribute compression using a volumetric approach: given a target volumetric attribute function $f : \mathbb{R}^3 \rightarrow \mathbb{R}$, we quantize and encode parameter vector $\theta$ that characterizes $f$ at the encoder, for reconstruction $f_{\hat{\theta}}(\mathbf{x})$ at known 3D points $\mathbf{x}$'s at the decoder.
Sandwiched Video Compression: Efficiently Extending the Reach of Standard Codecs with Neural Wrappers
We propose differentiable approximations to key video codec components and demonstrate that, in addition to providing meaningful compression improvements over the standard codec, the neural codes of the sandwich lead to significantly better rate-distortion performance in two important scenarios. When transporting high-resolution video via low-resolution HEVC, the sandwich system obtains 6. 5 dB improvements over standard HEVC.
Two Channel Filter Banks on Arbitrary Graphs with Positive Semi Definite Variation Operators
Our approach is based on novel graph Fourier transforms (GFTs) given by the generalized eigenvectors of the variation operator.
LVAC: Learned Volumetric Attribute Compression for Point Clouds using Coordinate Based Networks
We consider the attributes of a point cloud as samples of a vector-valued volumetric function at discrete positions.
3D Scene Compression through Entropy Penalized Neural Representation Functions
We unify these steps by directly compressing an implicit representation of the scene, a function that maps spatial coordinates to a radiance vector field, which can then be queried to render arbitrary viewpoints.
Spectral folding and two-channel filter-banks on arbitrary graphs
A major limitation is that this framework can only be applied to the normalized Laplacian of bipartite graphs.
Deep Implicit Volume Compression
We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in 3D voxel grids, and their corresponding textures.
Region adaptive graph fourier transform for 3d point clouds
Since clusters may have a different numbers of points, each block transform must incorporate the relative importance of each coefficient.
Graph-based compression of dynamic 3D point cloud sequences
This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes.
Precision Enhancement of 3D Surfaces from Multiple Compressed Depth Maps
In texture-plus-depth representation of a 3D scene, depth maps from different camera viewpoints are typically lossily compressed via the classical transform coding / coefficient quantization paradigm.
