English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

With the increased availability of 3D scanning technology, point clouds aremoving into the focus of computer vision as a rich representation of everydayscenes. However, they are hard to handle for machine learning algorithms due totheir unordered structure. One common approach is to apply occupancy gridmapping, which dramatically increases the amount of data stored and at the sametime loses details through discretization. Recently, deep learning models wereproposed to handle point clouds directly and achieve input permutationinvariance. However, these architectures often use an increased number ofparameters and are computationally inefficient. In this work, we propose basispoint sets (BPS) as a highly efficient and fully general way to process pointclouds with machine learning algorithms. The basis point set representation isa residual representation that can be computed efficiently and can be used withstandard neural network architectures and other machine learning algorithms.Using the proposed representation as the input to a simple fully connectednetwork allows us to match the performance of PointNet on a shapeclassification task while using three orders of magnitude less floating-pointoperations. In a second experiment, we show how the proposed representation canbe used for registering high-resolution meshes to noisy 3D scans. Here, wepresent the first method for single-pass high-resolution mesh registration,avoiding time-consuming per-scan optimization and allowing real-time execution.

Efficient Learning on Point Clouds with Basis Point Sets