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Project

Sparse Coding of Dynamic Point Clouds for Scene Analysis and Reconstruction (SPYDER) (FWOAL1048)

With recent expansion of sensors that capture depth information
along with other visual cues, three-dimensional (3D) point clouds
start to play a pivotal role in many applications, like autonomous
navigation, robotics, virtual and augmented reality. A proliferation of
devices such as RGB-D cameras and LiDAR leads to huge amounts
of 3D data that is being captured and analysed by various machine
vision systems. Since storing and processing 3D data points in their
raw form quickly becomes a bottleneck of a processing system,
designing compact, i.e., sparse representations to enable efficient
storage and analysis on the fly, is a major challenge. Applying
emerging deep learning models on point cloud data directly is not
possible because they are not structured and not ordered. Especially
challenging is processing of sequences of point clouds, also called
dynamic point clouds. While many recognition tasks benefit from
using temporal sequences of the monitored scene, processing of
dynamic point clouds is very difficult because they are not
represented on a regular spatio-temporal grid. Another crucial
problem is how to analyse raw point clouds without having to rely on
many annotated examples. There is a high demand for such
generative models for point clouds. The SPYDER project aims to
solve these challenges by developing a generic framework for
generative and scalable models for static and dynamic point clouds.
Date:1 Jan 2022 →  Today
Keywords:sparse coding, deep learning, point cloud processing
Disciplines:Computer vision, Data visualisation and imaging, Image and language processing, Pattern recognition and neural networks