Point clouds are becoming essential in key applications with advances in capture
technologies leading to large volumes of data. Compression is thus essential for storage …

Neural approximations of scalar-and vector fields, such as signed distance functions and
radiance fields, have emerged as accurate, high-quality representations. State-of-the-art …

Most neural compression models are trained on large datasets of images or videos in order
to generalize to unseen data. Such generalization typically requires large and expressive …

We present a novel method to provide efficient and highly detailed reconstructions. Inspired
by wavelets, we learn a neural field that decompose the signal both spatially and frequency …

Neural compression algorithms are typically based on autoencoders that require specialized
encoder and decoder architectures for different data modalities. In this paper, we propose …

A learning-based YOGA (Yet Another Geometry-based Point Cloud Compressor) is
proposed. It is flexible, allowing for the separable lossy compression of geometry and color …

Neural graphics primitives are faster and achieve higher quality when their neural networks
are augmented by spatial data structures that hold trainable features arranged in a grid …

Surveillance videos can capture a variety of realistic events and also anomalies. Due to an
increase in the crime rate in public areas, surveillance cameras are adopted in a very large …

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 …

3-D point clouds facilitate 3-D visual applications with detailed information of objects and
scenes but bring about enormous challenges to design efficient compression technologies …